Engineering Chemistry Volume 8

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Volume 8 of Engineering Chemistry

ISSN print 2813-6527
ISSN web 2813-6535

Full text available online at https://www.scientific.net/book/engineering-chemistry-vol-8/978-3-0364-1706-6

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Table of Contents

Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

Zetta Fazira, Mariana Hasyim, Erwinsyah Putra, Wijaya Pratama, Suraiya Suraiya, Syawaliah Muchtar, Asri Gani, Farid Mulana

Advancing Environmental Protection: Monolithic Bentonite and ZeoliteClay Adsorbents for Ion Removal

Darmadi Darmadi, Muhammad Zaki, Adisalamun Adisalamun, Aula Chairunnisak

Recyclability and Carbonization Regeneration of Oil Adsorbing Materials Based on Commercial Expandable Graphite

Chun Man Li

Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

Erwhyanta Monorizho Denesa, Tammy Laysandra, Felycia Edi Soetaredjo, Wenny Irawaty, Suryadi Ismadji, Jindrayani Nyoo Putro, Shella Permatasari Santoso

Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

Glenn Ongayo, Phoebe Ann Magallanes

Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

Alvan Ade Reza, Mahidin Mahidin, Yunardi Yunardi, Asri Gani, Sari Wardani

Determination of the Optimum Composition of Chitosan-Grafted Cellulose/TiO2 Films Using the Box Behnken Design Method

Haya Fathana, Surya Lubis, Muhammad Adlim, Rahmi Rahmi

The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

Nida Desri Effendhy, Roto Roto, Dwi Siswanta

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-chemistry-vol-8/978-3-0364-1706-6

Engineering Chemistry, ISSN: 2813-6535, Vol. 8, pp 1-12

doi: 10.4028/p-zQ9Ytg

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-25

Revised:
2024-03-15

Accepted:
2024-03-15

Online:
2024-12-13

Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

The original version of this paper is available on https://www.scientific.net/EC.8.1.pdf

Zetta Fazira , Mariana Hasyim , Erwinsyah Putra , Wijaya Pratama , Suraiya Suraiya , Syawaliah Muchtar , Asri Gani , Farid Mulana

¹Department of Chemical Engineering, Syiah Kuala University, Banda Aceh, 23111, Indonesia

Research Center of Environmental and Natural Resources, Syiah Kuala University, Banda Aceh, 23111, Indonesia

zettaf86@gmail.com, mariana@usk.ac.id, erwinsyah280402@gmail.com, wijayapratama833@gmail.com, suraiya.k@usk.ac.id, syawaliah@usk.ac.id, asri_gani@usk.ac.id, farid.mulana@usk.ac.id

Keywords: Patchouli dregs, pyrolysis, chloric acid, adsorption, methylene blue

Abstract

Increasing textile production leads to a corresponding rise in dye waste, including substances such as methylene blue. Methylene blue poses a significant environmental challenge due to its non-biodegradable nature and high toxicity, which can adversely affect both human health and ecosystems. To address this issue, various methodologies have been explored, with adsorption emerging as a promising technique. This study focuses on employing adsorption utilizing an adsorbent derived from patchouli dregs and activated using hydrochloric acid ( HCl ). The research commenced with the pyrolysis of patchouli dregs at different temperatures: , and for 1.5 hours. Subsequently, chemical activation was carried out using HCl solutions with concentrations of , and 0.7 M . The resulting activated adsorbent underwent characterization to assess its morphological structure, functional groups, and crystalline composition. The scanning electron microscopy (SEM) analysis revealed prominent pores in the patchouli dregs adsorbent post-activation, with a size of . X-ray diffraction (XRD) analysis demonstrated an irregular microcrystalline structure and amorphous nature of the activated patchouli dregs adsorbent. Additionally, Fourier transform infrared (FTIR) analysis identified active functional groups including , and , which facilitate methylene blue adsorption. Characterization of the various iterations of the patchouli dregs adsorbent confirmed its suitability for methylene blue adsorption, meeting the quality standards outlined in SNI 06-3730-1995. These standards include a water content of , ash content of , and iodine adsorption capacity of . In summary, this study elucidates the potential of patchouli dregs-derived adsorbents activated with hydrochloric acid for effective methylene blue removal, providing insights into their morphological, structural, and functional characteristics crucial for addressing the challenges associated with textile dye waste management.

Introduction

The original version of this paper is available on https://www.scientific.net/EC.8.1.pdf

Methylene blue, a non-biodegradable synthetic dye, poses a significant threat when released into surface and underground water due to its inherent toxicity to nervous tissue, reproductive systems, and skin. Conventional biological processes are ineffective in breaking down such non-biodegradable dyes, exacerbating the environmental and health risks associated with their uncontrolled emissions. Compounding the issue, methylene blue contains active ingredients like azo compounds (monoazo, diazo, triazo) known for their carcinogenic properties. Given its organic origin, methylene blue proves challenging to decompose [1], with permissible concentrations in water typically ranging from 5-10 . To address this, adsorption emerges as a promising method for removing methylene blue from liquid waste [3].

Several studies have addressed the removal of methylene blue from liquid waste. Maryudi et al. [4] investigated the impact of activated carbon size variations on methylene blue adsorption, considering concentration and contact time. Their findings revealed that smaller activated carbon
sizes, particularly those below 100 mesh, exhibited higher adsorption capacities, reaching up to . Optimal conditions for a low concentration of 15 ppm methylene blue were observed at 30 minutes, achieving full adsorption. In addition to activated carbon, Misran et al. [5] explored the potential of natural waste materials for real-world applications, specifically focusing on methylene blue dye waste adsorption. Their research utilized banana stem waste as a source of active carbon, demonstrating its effectiveness as raw material for adsorbent production and yielding promising results.

Mariana et al. [6] employed activated carbon derived from coconut fiber, modified with , to create adsorbents for ammonia and methylene blue adsorption. Additionally, Mariana et al. [7] utilized activated carbon sourced from coconut shells and fly ash for methylene blue adsorption. This composite achieved an impressive methylene blue adsorption capacity of up to , with a coconut shell to fly ash ratio of and an adsorption equilibrium time of 120 minutes. The process of utilizing biomass for adsorption is termed biosorption, and the adsorbent derived from it is referred to as a biosorbent.

Conversely, Indonesia stands as the world's foremost supplier of patchouli, contributing between to of the global market demand [8]. According to the Indonesian Directorate General of Plantations, by 2022, Indonesia's patchouli oil production is projected to reach 2,404 tons, cultivated across 18,076 hectares. This staggering output underscores Indonesia's role in satisfying approximately of the world's patchouli requirements. However, this substantial production also generates a significant amount of biomass waste. Therefore, there exists a compelling opportunity to optimize the utilization of patchouli oil production waste, aiming to mitigate the impact of other natural wastes, notably methylene blue.

Traditionally, the processing of patchouli dregs as sorbent involves carbonization process in furnaces. However, this approach raises environmental concerns due to its high energy consumption, typically reaching around . To address this issue, this research adopts a pyrolysis method, which consumes less energy compared to furnaces and yields a valuable by-product in the form of liquid smoke, offering further utilization opportunities. Moreover, this study specifically investigates the influence of pyrolysis temperature and acid concentration on the characteristics of the patchouli dregs adsorbent.

Experiment

The original version of this paper is available on https://www.scientific.net/EC.8.1.pdf

Materials. The materials utilized in this study comprise patchouli dregs sourced from local farmers in Aceh province, Indonesia, serving as the carbon source for producing adsorbents. Deionized water, obtained from the Environmental Laboratory of Chemical Engineering at USK, was used as a solvent. Additionally, , procured from Merck, Germany, served as the acid activator in the experiment.

Patchouli Dregs Adsorbent Production. The preparation of raw patchouli dregs before the activation process is crucial to eliminate impurities originating from their source. This involves washing the patchouli dregs thoroughly with clean, running water to prevent contaminants from reappearing. Subsequently, the patchouli dregs are dried in an oven at until a constant weight is achieved, effectively removing any residual moisture from the previous step. For the pyrolysis process, the equipment configuration depicted in Fig. 1(a) is utilized. Petroleum gas serves as the fuel to heat the pyrolysis reactor, with a temperature regulator situated on the burner to maintain the operating temperature. A jacket is employed to stabilize the operating temperature, minimizing heat loss during the process. Pyrolysis is conducted for a duration of 1.5 hours, with temperature variations set at 300,340 , and . The outcomes of the pyrolysis process on the patchouli dregs are illustrated in Fig. 1(b).

Fig. 1. Pyrolysis equipment scheme (a) and Patchouli dregs adsorbent after pyrolysis (b)

Fig. 1. Pyrolysis equipment scheme (a) and Patchouli dregs adsorbent after pyrolysis (b)

Acid Activation of Patchouli Dregs Adsorbent. The pyrolysis process yielded activated charcoal, tar, and liquid smoke as its primary products. The charcoal was subsequently ground and sieved through a 100 mesh size, resulting in a 12-gram portion selected for activation with 250 mL of HCl solution at concentrations of , and . Following through stirring for 1 hour, each mixture was left to stand for 24 hours [11]. Subsequently, the mixtures underwent filtration, followed by washing with distilled water until reaching a neutral . The activated charcoal was then dried in an oven at for 3 hours. Once dried, the resulting activated carbon was cooled in a desiccator at room temperature until completely dry. A comprehensive overview of the entire process is depicted in Fig. 2.

Fig. 2. Chemical activation process of patchouli dregs sorbent using HCl

Fig. 2. Chemical activation process of patchouli dregs sorbent using HCl

Water Content Analysis. A total of 2 grams of adsorbent was placed into a pre-weighed porcelain cup. The cup was then subjected to an oven temperature of for 24 hours, followed by cooling in a desiccator before being reweighed. The water content of the adsorbent was determined using Eq. (1) [12], where ' ' represents the initial weight of the adsorbent (in grams), and ' ' represents the final weight of the adsorbent (in grams).

Ash Content Analysis. Two grams of the adsorbent were placed into a previously weighed porcelain cup. The cup was then placed in a muffle furnace at for a duration of 3 hours. Afterward, it was allowed to cool in a desiccator before being reweighed. The ash content was determined using Eq. (2) [12], where 'a' represents the weight of the ash (in grams), and 'b' represents the initial weight of the adsorbent (in grams)..

Iodine Adsorption Analysis. 0.15 grams of the adsorbent was placed in a dark, enclosed container. Subsequently, 25 mL of 0.1 N iodine solution was added and stirred using a magnetic stirrer for 15 minutes before being filtered. A portion of 10 mL of the filtrate was then titrated with 0.1 N sodium thiosulfate solution. Upon nearing the disappearance of the yellow color in the solution, starch was introduced as an indicator, resulting in a color change to dark blue. The titration continued until the dark blue color faded away. The iodine adsorption number was determined using Eq. (3), where ' V ' represents the volume of sodium thiosulfate (in mL ), and ' N ' denotes the concentration of sodium thiosulfate.

Adsorbent Functional Group Analysis. Fourier-Transform Infrared Spectroscopy (FTIR) analysis which conducted using a SHIMADZU P-21 instrument was aimed to identify the functional groups present in the adsorbent. This characterization sought to pinpoint active groups crucial for methylene blue adsorption. The FTIR analysis was performed on patchouli dregs at various stages: before pyrolysis, after pyrolysis, after activation, and after adsorption. The infrared spectrum measurements employed the potassium bromide ( KBr ) pellet method, wherein patchouli dregs biosorbent were mixed with KBr in specified proportions and pressed using a hydraulic press to create a thin film. The FTIR spectrum was recorded across the range of .

Adsorbent Morphology. Scanning Electron Microscopy (SEM) analysis, conducted using a HITACHI TM 3000 instrument, was aimed to examine the surface morphology of the adsorbent material, including pores and surface area characteristics. For this analysis, 2 grams of patchouli dregs biosorbent were coated with gold or platinum and then placed onto the surface to be tested. The sample was then subjected to SEM imaging at a magnification of 2,000x.

XRD Analysis. X-ray diffraction (XRD) analysis, performed with a D-6000 instrument by SHIMADZU, aimed to ascertain the molecular shape and crystal structure of the adsorbent, crucial factors influencing the adsorption process. To conduct this analysis, 2 grams of patchouli dregs biosorbent powder were placed into an aluminum cuvette. Subsequently, the surface of the biosorbent powder was compacted and leveled using a glass piece. This process involved pressing the glass while moving it over the surface of the cuvette, ensuring a solid and flat surface parallel to the cuvette's surface. Finally, the sample was irradiated with X-rays for analysis.

Results and Disscussion

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Water Content. Assessing the water content of a biosorbent is crucial for understanding its hygroscopic characteristics. A lower water content enhances adsorption efficiency as it allows more sites within the pores to be available for adsorbate binding [13]. The relationship between temperature, HCl activator concentration, and the water content of the biosorbent derived from patchouli dregs is depicted in Fig. 3.

Fig. 3. Interconnection of biosorbent water contents with activator concentration and temperature

Fig. 3. Interconnection of biosorbent water contents with activator concentration and temperature

According to the findings presented in Fig. 3, the water content analysis of the patchouli dregs biosorbent aligns with the quality standards stipulated in SNI No. 06-3730-1995, wherein the water content within the adsorbent remains below . Notably, the research reveals that the water content in activated carbon falls within the range of . The biosorbent exhibiting the lowest water content is derived from a 0.5 M HCl -activated adsorbent subjected to pyrolysis at , registering a water content of . Conversely, the biosorbent with the highest water content originates from a 0.7 M HCl-activated adsorbent processed at , demonstrating a water content of . These results underscore the favorable adsorbent quality across all variations due to their low water content, facilitating the presence of large pores and an expansive adsorbent surface area [14].

Furthermore, the concentration of HCl activator and pyrolysis temperature exert notable influences on the water content in activated carbon, corroborating established theories and prior research [15]. Specifically, the water content tends to decrease with escalating concentrations of HCl activator and pyrolysis temperature in activated carbon. This phenomenon stems from the behavior of water molecules at temperatures exceeding and under 1 atm pressure, wherein they undergo a phase transition into gas. Consequently, water molecules dissociate from their carbon bonds, transitioning into a gaseous state. Utilizing HCl as a hygroscopic activator proves instrumental in optimizing the reduction of water content within the produced activated carbon. The diminished water content in activated carbon lowers the barrier for other molecules to access the pores, thereby enhancing adsorption efficiency [16].

Ash Content. The ash content parameter test serves the purpose of identifying the residual mineral content retained throughout the pyrolysis and activation processes. This residual content has the potential to obstruct the pores of activated charcoal, consequently diminishing its adsorption efficiency [15]. The interplay between temperature and the concentration of HCl activator in relation to the ash content of the biosorbent derived from patchouli dregs is illustrated in Fig. 4.

Fig. 4. Ash content on patchouli dregs adsorbent

Fig. 4. Ash content on patchouli dregs adsorbent

As depicted in Fig. 4, the average ash content analysis results for the patchouli dregs adsorbent fall within the range of 7-8%, meeting the adsorbent standards outlined in SNI No. 06-3730-1995, which stipulates a maximum ash content limit of . The biosorbent exhibiting the lowest ash content originates from a 0.3 M HCl -activated adsorbent subjected to a pyrolysis process temperature of , registering an ash content of . Conversely, the biosorbent with the highest ash content stems from a 0.7 M HCl -activated adsorbent processed at a pyrolysis temperature of , demonstrating an ash content of . These test results affirm the favorable adsorbent quality across all variations due to their low ash content, indicative of minimal blockage from mineral residues on the adsorbent surface [17].

The ash content test results depicted in Fig. 4 indicate a notable increase with escalating pyrolysis temperature and HCl activator concentration. Elevating the pyrolysis temperature correlates with higher ash content in activated carbon, attributable to the oxidation of inorganic elements forming ash compounds that adhere to the activated carbon surface [18]. Similarly, greater HCl activator concentrations result in higher ash content within the adsorbent. This trend arises from the heightened HCl concentration reducing water content, leading to the formation of mineral salts, some of which are insoluble and adhere to the pores of activated carbon [19].

Iodine Adsorption Capacity. The evaluation of iodine adsorption capacity aims to evaluate the ability of activated carbon to adsorb iodine, with higher iodine adsorption values indicating greater adsorption capacity of the adsorbent [20]. The relationship between temperature and HCl activator concentration with the iodine adsorption capacity of the biosorbent derived from patchouli dregs is illustrated in Fig. 5.

From Fig. 5, it is evident that the biosorbent exhibiting the highest iodine adsorption value originates from a 0.3 M HCl -activated adsorbent subjected to a pyrolysis process temperature of , registering an iodine adsorption value of . Conversely, the biosorbent with the lowest iodine adsorption value is derived from a 0.7 M HCl -activated adsorbent processed at a pyrolysis temperature of , demonstrating an iodine adsorption value of . Notably, the analysis results of the iodine adsorption capacity for each variation comply with the standards outlined in SNI 06-3730-1995, specifying a minimum iodine adsorption value of .

Fig. 5. The iodine adsorption capacity of patchouli dregs adsorbent

Fig. 5. The iodine adsorption capacity of patchouli dregs adsorbent

As depicted in Fig. 5, there is a notable decrease observed with increasing pyrolysis process temperature and HCl activator concentration. Elevated pyrolysis temperatures correlate with lower iodine adsorption, attributed to surface damage of the carbon caused by the high temperatures, leading to a brittle carbon structure [21]. Contrary to expectations, higher concentrations of the HCl activator tend to yield higher iodine adsorption capacity. However, in this study, the inverse relationship is observed due to excessively high HCl concentrations causing damage to the carbon pores formed [16]. Furthermore, the iodine adsorption capacity is influenced by the results of the ash content analysis. A lower ash content contributes to higher iodine adsorption capacity, as ash can potentially obstruct the pores of activated carbon, thereby reducing the surface area available for iodine adsorption [22].

The crystallinity of Patchouli Dregs Adsorbent. The XRD characterization was conducted to elucidate the crystalline structure of the patchouli dregs adsorbent before, after pyrolysis, and after chemical activation. Fig. 6 illustrates a comparison of the XRD analysis of the patchouli dregs adsorbent across these stages, characterized by peaks at temperature.

The X-ray diffraction results depicted in Fig. 6 unveil the crystalline structure evolution of the patchouli dregs adsorbent throughout the stages of pyrolysis and chemical activation. The diffraction patterns of the three samples closely align with the JCPDS standard (No. 41-1487), affirming the formation of a carbon crystal structure. Sharp peaks observed around 21-260, 440, and 640 for all samples solidify the presence of a crystalline carbon structure [23]. Notably, the XRD results postpyrolysis exhibit shallower peaks, suggesting that the pre-activation phase was not yet amorphous. This characteristic is evidenced by the absence of prominent peaks in the pre-activation samples. However, after activation, a distinct high peak emerges at an angle of 20: 25-30, indicative of a planar structure resembling the graphite hexagon observed in carbon-based materials. This structure indicates the presence of a semicrystalline phase in the activated carbon [24]. Moreover, the peaks observed in the XRD results post-pyrolysis demonstrate a regular structure with closely spaced peaks lacking sharpness, signifying the formation of amorphous activated carbon. This phenomenon suggests the involvement of OH groups from various organic materials in producing regular crystal structures within the activated carbon [25].

Fig. 6. XRD pattern of patchouli dregs adsorbent (a) before pyrolysis, (b) after pyrolysis, and (c) after acid activation

Fig. 6. XRD pattern of patchouli dregs adsorbent (a) before pyrolysis, (b) after pyrolysis, and (c) after acid activation

Morphology of Patchouli Dregs Adsorbent. The characterization of the adsorbent through Scanning Electron Microscopy (SEM) aimed to examine the morphological structure and pore size on the surface of the adsorbent. The SEM analysis results for the patchouli dregs waste adsorbent, both before and after pyrolysis and after chemical activation, are presented in Fig. 7.

Fig. 7 depicts the SEM findings on the patchouli dregs adsorbent treated before, after pyrolysis, and after chemical activation at a magnification of 2000 times. Image (a) illustrates the morphology of dried patchouli dregs before pyrolysis, exhibiting an absence of pores, necessitating the carbonization process for subsequent use as an adsorbent. Conversely, images (b) and (c) showcase the morphology of patchouli charcoal post-pyrolysis and after chemical activation, demonstrating suitable spaces for adsorbates to adhere. The activation of patchouli dregs charcoal using HCl aimed to enhance the adsorbent's dye adsorption capacity. Chemical activation was conducted using HCl at concentrations of , and 0.7 M to enlarge the pores by breaking hydrocarbon bonds or oxidizing surface molecules, thereby increasing the surface area and affecting adsorption efficiency [26].

Based on Fig. 7, the morphology of the three adsorbents exhibits a rough and hollow texture, except for the patchouli material before pyrolysis, which lacks pores. The patchouli dregs waste adsorbent before chemical activation displayed the smallest pore size at and the largest pore size at . Conversely, after chemical activation, the patchouli dregs waste adsorbent demonstrated the smallest pore size at and the largest pore size at . Comparing the pore sizes of these two adsorbents reveals that the patchouli dregs adsorbent after chemical activation has larger pores, indicating enhanced adsorption capability compared to its precursor. Furthermore, the patchouli dregs waste adsorbent before and after chemical activation belongs to the microporous material class, characterized by pore sizes of [27]. The pore size significantly influences the adsorbent's adsorption capacity, with larger pores exhibiting higher adsorbate adsorption tendencies [28].

Fig. 7. Patchouli dregs adsorbent morphology (a) before pyrolysis, (b) after pyrolysis, and (c) after acid activation

Fig. 7. Patchouli dregs adsorbent morphology (a) before pyrolysis, (b) after pyrolysis, and (c) after acid activation

Adsorbent Functional Group. Fig. 8 and Table 1 show that the spectra obtained have slightly different analysis results between patchouli dregs before and after pyrolysis and after activation. The difference can be seen in the spectrum of patchouli dregs before pyrolysis. O-H stretching can be seen as evidenced by a characteristic adsorption peak that is quite sharp and broad at wave number . In the FTIR wave spectrum before pyrolysis, an adsorption peak also appears at to , indicating the presence of and groups, typical groups found in activated carbon.

Table 1. Functional groups found in the adsorbent samples

Functional
Group
References
Wave number (cm⁻¹)
Before
Pyrolysis
After Pyrolysis
After
Activation

O-H (Hydroxyl)
4000-3400
-
3631
3612

O-H stretching
3500-3200
3311
-
-

C=H
3000-2800
2920
2947
2953

C=C
2450-2345
-
2351
2953

C-O
1470-1000
1334
1386
1282

C=O Keton
C=C Lignin
1700-1500
1641, 1622,
1525
1585
1697, 1606

C-O Carboxylic
Acid
1250-1000
1249, 1047
1056
1064

C-C
1000-500
601
842, 638
823, 609

Fig. 8. IR spectrum of patchouli dregs adsorbents

Fig. 8. IR spectrum of patchouli dregs adsorbents

The O-H group (stretching) no longer exists in the patchouli dregs spectrum after pyrolysis and after activation due to the pyrolysis process, which evaporates the water content in the patchouli dregs. Other research also states that the lower hydrogen and oxygen content in biochar after pyrolysis may be caused by dehydration, carbonization during activation, or due to the pyrolysis process [29].

Summary

The original version of this paper is available on https://www.scientific.net/EC.8.1.pdf

This work concludes that the patchouli dregs-based adsorbents especially after activated chemically demonstrates their compliance with quality standards set forth in SNI No. 06-3730-1995. Through XRD analysis, an amorphous structure with characteristic diffraction peaks, consistent with the JCPDS standard (No. 41-1487), was observed, indicating the presence of a carbon crystal structure. SEM analysis further elucidated the augmentation of surface area and pore structure due to chemical activation. Moreover, FTIR analysis identified key functional groups- , , and -within the adsorbent, serving as active sites for methylene blue adsorption. This comprehensive characterization underscores not only the fulfillment of quality criteria but also provides valuable insights into the structural and chemical attributes enhancing adsorption capabilities. These findings underscore the potential of HCl -activated patchouli dregs as effective adsorbents, offering promise for diverse applications in wastewater treatment and environmental remediation. Further research could delve into the adsorption performance, optimizing activation methods and exploring the adsorption mechanisms to harness the full potential of these biosorbents.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EC.8.1.pdf

The author is grateful to the Universitas Syiah Kuala for funding PPM-grant scheme with contract number: 1126/UN11/KPT/2023.

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The original version of this paper is available on https://www.scientific.net/EC.8.1.pdf

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A. Salah Omer, G.A. El Naeem, A.I. Abd-Elhamid, O.O.M. Farahat, A.A. El-Bardan, H.M.A. Soliman, and A.A. Nayl, Adsorption of crystal violet and methylene blue dyes using a cellulosebased adsorbent from sugercane bagasse: characterization, kinetic and isotherm studies, J. Mater. Res. Technol. 19 (2022).

V.A. Mentari and S. Maulina, Comparison of Functional Groups and Surface Morphology of Activated Carbon from Oil Palm Fronds Using Phosphoric Acid (H3PO4) and Nitric Acid (HNO3) Activators , Talent. Conf. Ser. Sci. Technol. 1 (2018).

D.H. Astuti, A. Kurnia, N. Fadilla, D.H. Astuti, A. Kurnia, N. Fadilla, Y.I. Mahendra, P. Studi, T. Kimia, F. Teknik,R. Rungkut, and M. Gunung, Quality Study of Adsorbent Composition Made from Raw Materials Quality Study of Adsorbent Composition With Sidoarjo, J. Tek. Kim. 14 (2020).

D.I.P. Kusumaningrum, D.H.A. Sudarni, and S. Wahyuningsih, Optimization of the Effect of Contact Time and Adsorbent Dosage of Eucalyptus Leaf Waste (Melaleuca cajuputi) using the Langmuir Adsorption Isotherm Method Optimization of the Effect of Contact Time and Adsorbent Dosage of Eucalyptus (Melaleuca cajuputi), J. Tek. Kim. USU. 11 (2022).

J.M. Jabar, M.A. Adebayo, I.A. Owokotomo, Y.A. Odusote, and M. Yılmaz, Synthesis of high surface area mesoporous ZnCl 2 -activated cocoa (Theobroma cacao L ) leaves biochar derived via pyrolysis for crystal violet dye removal, Heliyon. 8(2022).

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Engineering Chemistry, ISSN: 2813-6535, Vol. 8, pp 13-22

doi: 10.4028/p-J4atCj

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-25

Revised:
2024-04-23

Accepted:
2024-04-23

Online:
2024-12-13

Advancing Environmental Protection: Monolithic Bentonite and ZeoliteClay Adsorbents for Ion Removal

The original version of this paper is available on https://www.scientific.net/EC.8.13.pdf

Darmadi , Muhammad Zaki , Adisalamun , Aula Chairunnisak

¹Chemical Engineering Department, Universitas Syiah Kuala, 23111, Banda Aceh, Indonesia

darmadi@usk.ac.id, m.zaki@usk.ac.id*, adisalamun@usk.ac.id, aulachairunnisak@usk.ac.id

Keywords: clay; bentonite; zeolite; monolith; adsorption.

Abstract

Clay minerals possess substantial potential for developing innovative functional materials, particularly in the context of environmental protection. This study focuses on the adsorbent zeoliteclay and bentonite-clay, shaped into honeycomb monoliths to efficiently remove ions from water. The process involved physically activating powdered zeolite-clay and bentonite-clay through calcination at . The activated materials were then mixed with distilled water and molded into monolithic shapes through extrusion with stainless steel molds, resulting in cylindrical structures measuring 1.8 cm in diameter and 2 cm in height, featuring 40 perforations. Mechanical characterization aimed to evaluate structural strength and assess pressure drop during operation, revealing superior mechanical strength in bentonite-clay compared to zeolite-clay. The monolithic form exhibited lower pressure drop during operation compared to pellet adsorbents. In terms of adsorption performance, a batch reactor assessed efficiency, isotherm, and kinetics with 2 and 4 ppm ion solutions over a 240-minute period. The zeolite-clay monolith demonstrated the highest capacity, achieving a removal efficiency of up to . Maximal adsorption capacities for bentoniteclay and zeolite-clay were 0.209 and , respectively, with corresponding Langmuir adsorption equilibrium constants (KL) of 0.187 and by the Langmuir isotherm model. Kinetic analysis favored the pseudo-first-order non-linear model, indicating rates for zeolite-clay and bentonite-clay adsorbents at 2 and ion concentrations of , and . This study signifies a significant advancement in solid adsorbents, optimizing the adsorption process for broader applications.

Introduction

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The depletion of global water resources results from ecological damage caused by the release of harmful substances into natural surroundings, leading to significant water scarcity for essential applications [1]. Wastewater treatment offers a partial solution to the pressing global water crisis. A prevalent issue in water purification is the elevated levels of ionized iron found in groundwater worldwide, attributed to human activities like quarrying and natural geochemical cycles [2-3]. The rise in iron and other heavy metals in water ecosystems poses risks, as these elements are readily absorbed by living organisms, leading to bio-magnification and health problems. Excessive iron in the bloodstream can result in various medical issues, including mitochondrial dysfunction and tissue damage [4-5]. Despite its essential role in human health, elevated iron levels in groundwater can cause problems such as water discoloration, unpleasant taste, and textile and pipe darkening [7]. To address health concerns, the World Health Organization (WHO) has set a strict permissible limit for iron in drinking water at [8].

Current investigations indicate a range of methods, including chemistry, biology, and physiological processes are employed for eliminating metallic substances from water as well as wastewater. Amongst such things, adsorption is a widely passed strategy for eliminating a multitude of contaminants, such as textile hues and heavy metals [9]. Adsorption emerges as an exceedingly appealing approach owing to its notable efficacy and inherent simplicity. The utilization of this method is prevalent in the treatment of deleterious heavy metals owing to its efficacy and straightforward approach [10]. Clays are being evaluated as prospective and cost-effective alternatives due to their availability, high specific surface area, ion exchange capability, swelling
capacity, and cation exchange capacity [11-12]. Clay is a natural, non-toxic, abundant, durable, and high-surface area material extensively used in the adsorption process. Over the years, clay has been employed in various applications, including pottery and the production of construction materials such as bricks and tiles [13-14].
Bentonite clay, widely utilized across various industries, has applications in adsorbent production, cat litter, foundry work, and as a component in drilling and tunneling fluids, aiding lubrication, cooling, and cuttings removal while preventing blowouts [15]. Predominantly composed of montmorillonite, with two tetrahedral silica plates ( ) and an octahedral alumina plate ( ), bentonite clay's negatively charged surface allows it to expand upon contact with water. Heat treatment can modify its properties, reducing water-induced expansion and enhancing stability for dynamic system applications. Valuable for heavy metal and organic compound removal, bentonite clays possess a hydrophilic nature, high chemical stability, and cation exchange capacity [17].
In contrast, zeolites are inorganic crystalline minerals with structured pores smaller than two nm. Evolving types of zeolites include template-driven high-silica zeolites, natural zeolites, template-free low-silica zeolites, aluminum phosphate zeolites, and heteroatomic zeolites. Used in various sectors such as oil refining, fine chemicals, adsorption, and water treatment, manufactured zeolites are preferred due to large reserves, affordability, and essential constituents [18-20].
Honeycomb monoliths offer advantages, including reduced pressure drops, improved mass efficacy, and compelling physical and thermodynamic properties [21]. Two approaches are available for introducing active materials into the gas flow within these adsorbents: using multiple channels as a framework for an adsorbent film or directly molding monoliths with parallel channels, similar to carbon monoliths [22-24]. Previous research has demonstrated that naturally occurring clays can be easily shaped into honeycomb monoliths, utilized for various ecological purposes, including VOC adsorption, methylene blue removal from water, and biofuel combustion [25]. Clay monolithic adsorbents have been customized for the targeted removal of iron, mercury, and lead contaminants in water [26-29]. The structured pores of natural clays exhibit excellent handling features when exposed to water, enhancing the removal of contaminants during adsorption processes [30].
In the present study, monolith adsorbents were fabricated using zeolite and bentonite materials to investigate their efficacy in adsorbing Fe (II). These monoliths were meticulously prepared by adopting a stable structure through the use of a stainless-steel molder, involving a series of processes encompassing mixing, molding, thermal consolidation, and subsequent drying. Mechanical attributes, such as pressure drop and mechanical strength, were also rigorously examined, as they hold a critical role in the adjustment of operational parameters. The study encompassed an evaluation of the monoliths' performance, involving the assessment of their adsorption efficiency, isotherm behavior, and kinetic characteristics. Furthermore, the potential for future application of earth's resources such as clay, bentonite, and zeolite as customized adsorbents in a monoliths way, as explored in the current research, encompasses the potential to significantly contribute to the elimination and eradication of pollutants from water and wastewater systems.

Experimental

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Monolith preparation. The primary materials for crafting monolith adsorbents consisted of clay, zeolite and bentonite. The Bentonite utilized in the current research was purchased through Java Indah Ltd., located in West Java, Indonesia. Similarly, the zeolite and clay samples were obtained from Sigli, a region within the Pidie District of Aceh Province, Indonesia. To properly to prepare the materials, a thorough washing process was conducted on both the clay and zeolite samples. Initially, the samples were washed with distilled water for a duration of 5 minutes. Subsequently, numerous decanting cycles were performed to effectively remove any impurities present in the samples. Then, that resulting slurry was then exposed to a two-day period of air-drying in order to eliminate the remaining water on the surface. The dry clay and zeolite were finally crushed in a ball mill. To ensure homogeneity, clay, zeolite, and bentonite were powdered and sieved through a mesh sieve. Each zeolite and bentonite was then combined with clay in a 2:1 ratio to achieve consistency, with a sufficient amount of water added to form an ideal paste appropriate for molding. The molded
monoliths were allowed to air-dry for two days and were later subjected to calcination at for a duration of 3 hours and stored in vacuum jar.

Characterization of monolith. The study primarily focused on characterizing the adsorbent's mechanical properties, particularly compressive strength and pressure drop. The analyses were essential for understanding how changes in pressure affect the adsorbent structure. Compressive strength was analyzed using a hydraulic universal testing machine (WAW-E-Series). In practical applications for measuring pressure drop within a column, monolithic adsorbents are arranged in series within an adsorption column and packaged for distribution, necessitating a robust structure. Pressure drop analysis involved various adsorbent forms, such as pellets and monoliths. It was conducted using an adsorption column equipped with water pressure sensors at the inlet and outlet, programmed with an Arduino Uno microcontroller to measure pressure differences.

Adsorption process. To prepare the metal solution, the iron standard solution (Merck) 1000 ppm was diluted to concentrations of 2 and 4 ppm . In the adsorption process, each zeolite and bentonite monolith were immersed in 250 mL of solutions with concentrations of 2 and 4 ppm in separate batch reactors ( 250 mL Erlenmeyer flasks). The experiment was performed under ambient conditions, specifically at room temperature, with a mixing rate of 110 rotations per minute (rpm) for an entire period of 240 minutes. Following this, 10 mL of the filtrate was extracted, and its absorbance was measured using Atomic Absorption Spectroscopy (AAS) (Shimadzu AA6300). The present study assessed the effectiveness of the adsorption process through investigation of multiple variables, including adsorption efficiency, isotherm models (namely Langmuir and Freundlich), and kinetics equations (either pseudo-first order or pseudo-second order).

Result and Discussion

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Monoliths Characteristics. Compressive strength assessments were conducted to ascertain the loadbearing capacity of zeolite and bentonite adsorbents. The findings obtained through these tests are displayed in Table 1, which includes data on the applied force or load (F, measured in Newtons), the compressive surface area (A, in square millimeters), and the resulting compressive strength (Fc, expressed in Megapascals, MPa) of the monolith adsorbent.

Table 1. Results for compressive strength of monolith adsorbent

Sample | F (N) | A (mm²) | Fc (MPa)
Zeolite | 608 | 241.78 | 2.51
Bentonite | 2711 | 166.18 | 16.31

Analysis of Table 1 reveals that bentonite exhibits the highest compressive strength among the materials tested, owing to its denser pore structure in comparison to the other materials. The compressive strengths of zeolite and bentonite monolith adsorbents were 2.51 MPa and 16.31 MPa , correspondingly. Aside from the chemical bonding factor inside the monolith matrix, increasing porosity might result in a reduction in compressive resistance [31]. It can be inferred that the concentration of zeolite powder has a significant impact on the material's load-bearing capacity [32]. Notably, the compressive strength of the zeolite adsorbent decreases, primarily due to its higher porosity compared to the bentonite monolith.

Pressure drops. Pressure drop is the pressure loss as the fluid passes through the bed. In an adsorption system, pressure drop plays a significant role in purifying a material. A system with too low a pressure drop will have ruined circulation in a bed. Meanwhile, a high-pressure drop of adsorbent will cause more consumption of power to be fed in a packed column [33]. To make sure the adsorbent has the specific pressure drop, calculations in various types of adsorbents at various heights have been done. The results of the pressure drop measurement can be seen in Figure 1.

Fig. 1. Pressure drops in the fixed bed adsorber for various types of clay-based adsorbents

Fig. 1. Pressure drops in the fixed bed adsorber for various types of clay-based adsorbents

The monolith showed a lower pressure drop than the pellet at the three fixed bed heights. At the height of 40 cm , the monolith and pellet pressure drop differ slightly. At the height of 60 cm and 100 cm , the pressure drop of the monolith is also lower than the pellet. Because of Ergun's Equation about pressure drop in a packed bed, solid adsorbents with smaller diameters tend to have a high-pressure drop. Monolith pressure drop performance in a packed bed has a lower pressure drop because of its unique structure. It has a high porosity value because of the presence of channels or cavities [34].

Adsorption Capacity. In this study, the efficiency of zeolite and bentonite monoliths in adsorbing metal ions were assessed at various contact times, including , and 240 minutes, as depicted in Figure 2a and 2b.

Fig 2. A correlation exists among process period and effectiveness at varied initial concentrations
(a) 2 ppm and (b) 4 ppm

Fig 2. A correlation exists among process period and effectiveness at varied initial concentrations (a) 2 ppm and (b) 4 ppm

These figures portray the relationship among the efficiency and period process. Particularly, there was a substantial elevation in the adsorption efficacy observed during the 240 -minute duration, indicating that prolonged contact periods promote enhanced interactions between the metallic ions and the adhesion domains of the adsorbent. The maximum efficiency for adsorption was observed to be when utilizing zeolite monoliths with a solution. Conversely, bentonite monoliths exhibited a maximum removal efficiency of at a concentration. Notably, the two figures demonstrate that the equilibrium stages are reached at 240 minutes, as seen by the stagnation of efficiencies at 200 and 240 minutes. Currently, the adsorption capacity has exhibited stability, providing an indication that the active sites on the adsorbents have reached a state of near-full closure, thus finishing the interactions between the adsorbate and adsorbent. Particularly, the utilization of zeolite monoliths for ion adsorption outperformed bentonite due to higher adsorption kinetics due to zeolite's higher cation-exchange potential in compared with bentonite [35].

Isotherm of Adsorption. The adsorption isotherm shows the results of stationary adsorption studies in an instance for a particular period until a state of equilibrium has been reached and defines the adsorbent's maximum performance and adsorbate solution's interaction [36]. Data from batch experiments on equilibrium adsorption were examined through a non-linear approach for identifying the best isotherm model among Langmuir and Freundlich. To determine and optimise the isotherm parameters in non-linear equations, the solver plugin function in Microsoft® Excel was employed. The model with the least SSE (Sum of Square Error) result was identified as the best isotherm model. SSE is recognized as the value of the least objective function for obtaining the optimal isotherm parameters and minimizing the gap between theoretical and experimental data [37].
The formula for equation 1 is the mathematical representation of the Langmuir model [38]:

In this context, qe represents the adsorption capacity of mercury ( Hg ) in milligrams per gram ( ), Ce denotes the equilibrium concentration of Hg in milligrams per liter ( ), and aL are the Langmuir coefficients.
The equation for the Freundlich model can be written as follows [38]:

Within this investigation, the parameters under consideration are defined as follows: qe, denoting the capacity for mercury (Hg) adsorption, expressed in milligrams per gram ( ); Ce, representing the equilibrium concentration of Hg in milligrams per liter ( ); Kf , indicative of the predicted capacity in the Freundlich Model, measured in (mg/); and n , signifying the intensity of the reaction. Table 2 encapsulates a succinct summary of the optimization procedures derived from the isotherm analysis. The assessment, rooted in the Sum of Squares of Errors (SSE) values, points towards the Langmuir isotherm model exhibiting a commendable fit to the adsorption study. Specifically, the constants KL and aL, pertinent to both zeolite-clay and bentonite-clay adsorbents, are quantified as , and , respectively. In accordance with the Langmuir isotherm, the adsorption process transpires on the adsorbent surface, and the active sites within the outermost layer are characterized as both homogeneous and finite. This mechanism involves the formation of a singular layer (monolayer) on the adsorbent surface, consisting of homogeneous and energetically equivalent active sites. These sites possess a consistent binding energy, ensuring that the adsorbent selectively binds to only one metal ion per active site, with no interactions among adjacent ions [39].

Table 2. A concise overview of thermodynamic equilibrium and isotherm data.

Adsorbent
Langmuir Isotherm
Freundlich Isotherm

KL
(L/g)
aL
(L/mg)
Qo
(KL/aL)
SSE
Kf
(mg/g)
N
SSE

Zeolite-clay
0.187
0.895
0.209
3.08×10⁻⁴
0.064
1.46
4.42×10⁻³

Bentonite-clay
0.181
0.639
0.284
4.32×10⁻⁴
0.074
1.37
5.98×10⁻³

Kinetics Study. Adsorption kinetics plays a crucial role in elucidating the rate at which iron ions are absorbed, directly associated with the residence time of these ions on the adsorbent's surface. To ascertain the kinetic rate and mechanism governing the adsorption of ions onto the adsorbent, two widely employed models, namely the Pseudo First Order rate equation and the Pseudo Second Order equation, are employed [40]. A comparative analysis between these two models is conducted to determine which one better aligns with the experimental data through regression analysis. This comparison involves both linear methods that evaluate the compatibility of equations with experimental observations and non-linear methods that rely on data optimization processes and numerical techniques aimed at minimizing error values [41]. The results of the data processing pertaining to adsorption kinetics are presented in Table 3.

Tabel 3 The adsorption kinetic tabulation data

To determine the most suitable adsorption kinetics model, a comparison of the regression coefficient ( ) between linear and non-linear models is essential [42]. Examining Table 2, it is evident that at solution concentrations of and , the non-linear method yields higher values ( ) compared to the relatively lower values obtained through the linear method. For solutions with concentrations of and , the adsorption kinetics of zeolite and bentonite monoliths align with the pseudo-first order model, as indicated by the smallest Sum of Squared Errors (SSE). This is further substantiated by the proximity of the theoretical adsorption capacity values (qe, calc) obtained from the pseudo-first-order kinetic model to the experimental adsorption capacity values (qe, exp). The pseudo-first-order kinetic model implies that the rate of ion absorption is directly proportional to the availability of active and free ion bonds on the surface of the adsorbent [43]. In accordance with the model, iron ion molecules are assumed to bind to a single active site on the adsorbent's surface, characterizing the adsorption as physical in nature. The pseudo-first-order kinetics is particularly suited for adsorption processes in low-concentration solutions, which aligns with the relatively low concentration of the solutions used in this study, specifically 2 and [44].
For a solution concentration of , the pseudo-first-order adsorption rate constant (k1) was determined to be for the zeolite adsorbent and for the bentonite monolith. At a solution concentration of , the values for were for zeolite and 0.003852 for bentonite. This trend is in accordance with the theory that posits an inverse relationship between the adsorption rate constant and the initial concentration of the solution. As the concentration of the solution increases, the adsorption rate constant tends to decrease, as it requires more time for the process or reaction to attain equilibrium[45].

Summary

The original version of this paper is available on https://www.scientific.net/EC.8.13.pdf

To sum up, this study has successfully developed a unique and compact adsorbent, referred to as monolith, utilizing clay-zeolite and bentonite for the purpose of removing iron from aqueous solutions. Notably, in terms of mechanical properties, bentonite exhibited higher compressive strength, reaching a maximum of 16.3 MPa , in comparison to zeolite. The assessment of pressure drop underscored the monolith's advantage, as it displayed a lower pressure drop compared to pellettype adsorbents. The efficiency of adsorption highlighted zeolite's exceptional performance in
removing , pointing at Langmuir equation model in whole adsorption process also achieving an impressive removal efficiency of , attributed to zeolite's inherent binding capabilities. Furthermore, the kinetics study indicated the most suitable order, with the pseudo-first-order model demonstrating the highest value and the lowest SSE value, with a kinetic rate of . Based on the outcomes of the study, it becomes evident that zeolite monoliths display a notable capacity for adsorbing from water sources. The experimental findings highlight the efficiency and effectiveness of zeolite-based monoliths in the removal of ions from aqueous solutions. This characteristic underscores the potential of zeolite monoliths as a promising and reliable adsorption material for addressing water contamination issues, particularly in scenarios where the presence of poses environmental and health concerns. The development and application of such innovative adsorbents contribute to sustainable solutions for ensuring water quality and safeguarding both the environment and human health.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/EC.8.13.pdf

The authors wish to convey their deep appreciation to the Process Technology Laboratory within the Department of Chemical Engineering at Universitas Syiah Kuala for their invaluable assistance in providing access to the necessary experimental equipment. This research project received vital financial support through the Syiah Kuala University grant, specifically under Project No. 78/UN11.2.1/PT.01.03/PNBP/2021. The authors are profoundly thankful for this support.

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C. García-Carvajal, J. Villarroel-Rocha, D. Curvale, M. M. Barroso-Quiroga, and K. Sapag, "Arsenic (V) removal from aqueous solutions using natural clay ceramic monoliths," Chem Eng Commun, vol. 206, no. 11, pp. 1440-1451, Nov. 2019, doi: 10.1080/00986445.2018.1564910.

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Engineering Chemistry, ISSN: 2813-6535, Vol. 8, pp 23-29

doi: 10.4028/p-r32WGe

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-04-22

Revised:
2024-09-01

Accepted:
2024-09-04

Online:
2024-12-13

Recyclability and Carbonization Regeneration of Oil Adsorbing Materials Based on Commercial Expandable Graphite

The original version of this paper is available on https://www.scientific.net/EC.8.23.pdf

Chunman Li*

Pipe China Institute of Science and Technology, Langfang 065000, PR China

*E-mail: Icmg72@sina.com

Keywords: Expanded graphite, oil adsorbing material, adsorption capacity, recyclability, carbonization regeneration

Abstract

This study focused on the oil absorption and carbonization regeneration properties of expanded graphite (EG) prepared from commercial expandable graphite as an oil absorbing material for different oils. The specific surface area of the prepared EG sample was , and the oil absorption was as high as . More importantly, the prepared expanded graphite had excellent recyclability and good carbonization regeneration performance. Even after 5 cycles, the adsorption capacity remained above . The regenerated EG sample regained a high oil absorption capacity of . This article has important guiding significance for promoting the large-scale practical application of expanded graphite oil absorbing materials.

Introduction

The original version of this paper is available on https://www.scientific.net/EC.8.23.pdf

In recent years, with the development of the petroleum industry, there has been an increase in offshore oil spills and the discharge of oily wastewater worldwide, leading to increasingly serious environmental oil pollution. The oil spill has caused serious environmental damage to marine water resources, posing a significant threat to human survival and ecosystems [1]. Adsorption method is the use of adsorbents to adsorb organic matter, inorganic salts, and oils in wastewater, achieving the goal of cleaning up pollution[2]. It is one of the effective methods for treating polluted wastewater and marine oil spills. Commonly used adsorbents include natural waste such as carbon-based materials, diatomaceous earth, fly ash, and sawdust. But the oil absorption capacity of most natural waste is relatively low. The oil adsorption capacity of the natural adsorbent for corn cob (Zea mays L.) powder is only [3], the absorption capacity of discarded durian peel is [4], the maximum oil absorption capacity of sawdust particles is [5], the maximum oil absorption capacity of fly ash achieves at [6], and the oil adsorption capacity of activated carbon is [7]. Although delignified wood [8], electrospun cellulose fiber three-dimensional aerogel [9] and super hydrophobic cotton [10] have high oil absorption capacity of , the process of these absorbents is complex, which is not conducive to large-scale practical application. As one of carbon-based material, expanded graphite ( EG ) is a worm like porous material obtained from natural graphite flakes after intercalation, water washing, drying, and high-temperature heat treatment [11]. EG has a worm like structure and developed pore structure. Compared with commonly used activated carbon adsorption material, the oil adsorption capacity of EG is about ten times that of activated carbon, making it an ideal material for treating oil pollution[12-14]. The present reports mainly focus on the preparation and the oil absorption property of EG prepared from expandable graphite in the laboratory [12-18]. However, there have been no reports on the oil absorption and regeneration performance of EG oil absorbing materials based on commercial
expandable graphite, which is not conducive to the large-scale practical application of EG oil absorbing materials. Herein, EG based on commercial expandable graphite was used as the absorbing materials of different oil, and its oil absorption performance and carbonization regeneration performance were focused on. This paper has important guiding significance for promoting the large-scale practical application and development of expanded graphite oil absorbing materials.

Experimental Method

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Commercial expandable graphite was purchased from Qingdao Chenyang Graphite Co., Ltd of China. Commercial expandable graphite was heat-treated at for 60 seconds in a muffle furnace to form EG sample. The oil adsorption performance of EG was tested by adsorption experiment according to the reported method [17,18]. The oil adsorption performance of EG was tested by adsorption experiment. All tests were performed at . Simply put 50 ml of oil materials in a beaker, and then about 0.1 g of EG is weighed and poured into the oil and stirred to stand. At the end of the experiment, the EG was filtered out of the oil to drip the residual liquid on the surface for 2 min , and finally weighed. Then the oil absorption capacity ( m ) was calculated with the following equation (Eq.1) to evaluate the oil absorption capacity of the EG sample,

where, m is the adsorption capacity of EG at a certain time ( 3 min is the weight of EG after oil absorption, is the initial weight of .

Heating and evaporative condensation processes under vacuum was utilized to recover EG and oil [17]. The EG sample after oil adsorption was heated to the boiling point of oil sample under 0.01 MPa pressure to evaporate, and the condensation recovery device was used to collect the oil (the weight was recorded). After heating, the remaining part of the tube was the recycled EG ((the weight was recorded)). The recycled EG was further used to adsorb oil and repeated times to determine the recyclability. The carbonization regeneration of the EG sample after adsorption and recovery of oil (after 10 times recycle) was carried out in a vacuum tube furnace at for 1 h under different atmosphere (vacuum or nitrogen). The recyclability of the regenerated EG sample was also tested.

The phase composition of all the expanded graphite samples were tested by X-ray diffraction (XRD) on a Philips X'pert diffraction instrument with incident radiation ( ), the morphology and the microstructures were analyzed by scanning electron microscope (SEM, Hitachi 8010), the chemical groups were characterized by Fourier transform infrared spectrometer (FTIR, TENSOR27, Bruker, Germany), and the Brunauer-Emmet-Teller (BET) specific surface area was detected by Nitrogen sorption (Quantachrome Corporation, USA). The pore size distribution was plotted based on the Barrett-Joyner-Halenda (BJH) method.

Results and Discussion

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Fig. 1 illustrated SEM images of the initially prepared EG sample and the regenerated EG samples. It could be seen that all the three EG samples was composed of many adhered and stacked graphite flakes. The graphite flakes were interwoven with each other, and a large number of interconnected micro-scaled pores were formed. The layered structure and a large number of pores
are conducive to improving the adsorption capacity of EG. More importantly, the EG samples had high specific surface area and large pore volume which is very important for the adsorption performance of adsorption material[16-18], and the graphite flakes possessed mesoporous structure, as shown in Fig. 2. The initially prepared EG sample from commercial expandable graphite by heat-treating at had a specific surface area of and a pore volume of with a pore diameter of 30.7 nm . The specific surface area of the regenerated EG sample carbonized at under nitrogen condition and that under vacuum condition decreased to and , respectively, but the pore diameter of the two samples increased to around 49 nm .

Fig. 1 SEM images of (A) and (B) the initially prepared EG and the regenerated EG samples: (C) and (D) at nitrogen atmosphere, (E) and (F) at vacuum atmosphere

Fig. 1. SEM images of (A) and (B) the initially prepared EG and the regenerated EG samples: (C) and (D) at nitrogen atmosphere, (E) and (F) at vacuum atmosphere

Fig. 2 (A) nitrogen adsorption desorption isotherms and (B) BJH pore size distribution curves of different EG samples: (a) the initially prepared EG, (b) EG regenerated at nitrogen atmosphere, and (c) EG regenerated at vacuum atmosphere

Fig. 2. (A) nitrogen adsorption desorption isotherms and (B) BJH pore size distribution curves of different EG samples: (a) the initially prepared EG, (b) EG regenerated at nitrogen atmosphere, and (c) EG regenerated at vacuum atmosphere

It was precisely because of its unique loose porous structure that the EG samples had good adsorption performance for macromolecular compounds. The initially prepared EG sample from commercial expandable graphite by heat-treating at demonstrated an adsorption capacity of dimethyl silicone oil as high as and over for other kind oil (see Table 1). And also the initially prepared EG sample had excellent recyclability, after 5 times recycle the adsorption capacity still kept over (as shown in Fig. 3A). After 10 times recycle, the adsorption capacity decreased to . Interestingly, after carbonization regeneration, the EG sample of 10 times recycle reacquired a high oil adsorption capacity of , as shown in Fig. 3B. And 3 times recycle the adsorption capacity still kept over . Table 2 gives the comparison of oil adsorption capacities of the reported absorbents [4, 5-10, 18]. The oil absorption capacity of the EG sample prepared in this study is much higher than that of most natural adsorbents. By the way, the recovery rates of dimethyl silicone oil, soybean oil, lubricating oil, liquid paraffin, and circulating pump oil are , and , respectively, which decrease as the boiling point decreases. The ratio of after the recycling process to before the first cycle is approximately 0.985 . This means that the EG absorbent lost after a single recovery process. Fig. 4 is the FTIR image of the prepared EG sample.

Table 1 The adsorption capacity of the initially prepared EG sample for different oil

Oil type | Dimethyl silicone oil | Soybean oil | Lubricating oil | Liquid paraffin | Recycle pump oil
Oil boiling point | ~220°C | ~260°C | ~290°C | ~270°C | ~300°C
m₁ (g) | 0.1035±0.02 | 0.104±0.02 | 0.1035±0.01 | 0.1045±0.03 | 0.1005±0.04
m₂-m₁ (g) | 7.8690±0.04 | 6.8485±0.02 | 6.1155±0.03 | 6.1245±0.04 | 5.0800±0.06
Adsorption capacity (g/g) | 76.03±0.04 | 65.85±0.02 | 59.09±0.03 | 58.61±0.04 | 50.28±0.06

Fig. 3 (A) the adsorption recyclability of the initially prepared EG sample for dimethyl silicone oil over 10 cycles and (B) the adsorption recyclability of the EG sample regenerated at nitrogen atmosphere for dimethyl silicone oil over 5 cycles

Fig. 3. (A) the adsorption recyclability of the initially prepared EG sample for dimethyl silicone oil over 10 cycles and (B) the adsorption recyclability of the EG sample regenerated at nitrogen atmosphere for dimethyl silicone oil over 5 cycles

Table 2 Comparison of oil adsorption capacities of the reported absorbents

Absorbents | Oil type | Absorption capacity (g/g) | Reference
EG | Dimethyl silicone oil | 76 | this work
EG | Pump oil | 50.28 | this work
EG | Soybean Oil | 65.85 | this work
Discarded durian peel | Cooking oil | 1.301 | [4]
Sawdust particles | Engine oil, Soybean oil | 2.216, 1.9 | [5]
Activated carbon | vegetable oil, crude oil | 1.4, 1.6 | [7]
Delignified wood | Silicone oil | 37 | [8]
Three dimensional cellulose fiber aerogel | Pump oil | 27.26 | [9]
Super hydrophobic cotton | Pump oil | 44 | [10]
EG-3 (expanded graphite by microwave irradiation) | Hydraulic oil, vegetable oil | 235, 86 | [18]

Fig. 4 The FTIR image of the prepared EG sample

Fig. 4. The FTIR image of the prepared EG sample

The reason why the EG sample has the selective absorption for dimethyl silicone oil is possibly due to the EG sample surfaces possess and-OH groups as shown in Fig. 4 (the broad absorption peak at is caused by the stretching vibration of -OH , while at it is caused by the stretching vibration of ). These and -OH groups can interact with group (such hydrogen bonding), leading to the EG sample has the selective absorption for dimethyl silicone oil.

Conclusions

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Generally speaking, the prepared expanded graphite sample based commercial expandable graphite possessed a large number of interconnected micro-scaled pores with a specific surface area of , and demonstrated an adsorption capacity of dimethyl silicone oil as high as . And the prepared expanded graphite had excellent recyclability and good carbonization regeneration performance.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EC.8.23.pdf

This work was supported by Technology Development Project of PipeChina Institute of Science and Technology (Grant No. ZYZL-DTXNY-202204).

References

The original version of this paper is available on https://www.scientific.net/EC.8.23.pdf

Carpenter, A., Oil pollution in the North Sea: the impact of governance measures on oil pollution over several decades. Hydrobiologia 845, 109-127, (2019).

Bai, Y.R., Dai, L.Y., Sun, J.S., Jiang, G.C., Lv, K.H., R.C., Cheng, Shang, X.S., Plugging performance and mechanism of an oil-absorbing gel for lost circulation control while drilling in fractured formations. Petroleum Science 19, 2941-2958, (2022).

Haryanto, B., Nasution, I.T., Polem, A.F., Tambun, R., Alexander, V., Natural adsorbent of corncob (Zea mays L.) powder capability in purification of used cooking oil with shaking operation based on turbidity. Results in Engineering 19, 101233, (2023).

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Sun, J., Li, R., Wang, X.Y., Zhao, C.H., Song, Q.W., Liu, F., Wang, Z.H., Liu, C.S., Zhang, X.X., Marine oil spill remediation by Candelilla wax modified coal fly ash cenospheres. Chemosphere 330, 138619, (2023).

Kyaw, H.H., Myint, M.T.Z., Al-Belushi, M.A., Dobretsov, S., Al-Abri, M., Nanomaterial grafted polymorphous activated carbon cloth surface for antibacterial, capacitive deionization and oil spill cleaning applications. Chemosphere 350, 141053, (2024).

Yang, R., Cao, Q.H., Liang, Y.Y., Hong, S., Xia, C.L., Wu, Y.J., Li, J.Z., Cai, L.P., Sonne, C., Le, Q.V., Lam, S.S., High capacity oil absorbent wood prepared through eco-friendly deep eutectic solvent delignification. Chemical Engineering Journal 401, 126150, (2020).

Zhang, Y.R., Wang, Y.X., Construction of three-dimensional aerogels from electrospun cellulose fibers as highly efficient and reusable oil absorbents. Separation and Purification Technology 353, 128604, (2025).

Wu, D., Wang, T.S., Hu, S.Y., Wu, W.B., Lu, B.B., Huang, X.Y., Yu, W., Wang, M., Wang, G.G., Zhang, J.H., Solvent-free processing of eco-friendly magnetic and superhydrophobic absorbent from all-plant-based materials for efficient oil and organic solvent sorption. Science of the Total Environment 800, 149558, (2021).

Bannov, A.G., Timofeeva, A.A., Shinkarev, V.V., Dyukova, K.D., Ukhina, A.V., Maksimovskii, E.A., Yusin, S.I., Protection of Metals and Physical Chemistry of Surfaces 50(2), 183-190, (2014).

Yang, H.W., Bian, S., Hu, J.Q., Li, F.B., Yao, T., Effect of water chemistry on the adsorption of lubricating oil on oxidized graphite. Journal of Molecular Liquids 219, 1157-1160, (2016).

Ding, X.H., Wang, R., Zhang, X., Zhang, Y.Z., Deng, S.H., Shen, F., Zhang, X.H., Xiao, H., Wang, L.L., A new magnetic expanded graphite for removal of oil leakage. Marine Pollution Bulletin 81, 185-190, (2014).

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The original version of this eBook is available on https://www.scientific.net/book/engineering-chemistry-vol-8/978-3-0364-1706-6

Engineering Chemistry, ISSN: 2813-6535, Vol. 8, pp 31-40

doi: 10.4028/p-3VojWw

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-09-11

Revised:
2024-11-23

Accepted:
2024-11-28

Online:
2024-12-13

Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

The original version of this paper is available on https://www.scientific.net/EC.8.31.pdf

Erwhyanta Monorizho Denesa , Tammy Laysandra , Felycia Edi Soetaredjo , Wenny Irawaty , Suryadi Ismadjii , Jindrayani Nyoo Putro , Shella Permatasari Santoso

Department of Chemical Engineering, Master Programme, Widya Mandala Surabaya Catholic University, Kalijudan 37, Surabaya 60114, Indonesia

Department of Chemical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd, Da'an District, Taipei City, Taiwan 106

Department of Chemical Engineering, Widya Mandala Surabaya Catholic University, Kalijudan 37, Surabaya 60114, Indonesia

erwhyanta@gmail.com, tammylaysandra@gmail.com, felyciae@yahoo.com, wenny@ukwms.ac.id suryadiismadji@yahoo.com, jindrayani@ukwms.ac.id, shella@ukwms.ac.id

Keywords: Adsorption kinetics; pseudo-first-order; pseudo-second-order; competition parameter; multicomponent system.

Abstract

The investigation of kinetics is crucial for optimizing the adsorption process, particularly in multicomponent systems where several adsorbates compete for active sites on the adsorbent particle. This study presents a novel approach to improve the pseudo-first-order and pseudo-secondorder kinetic models by integrating a competition parameter. This parameter quantifies the influence of the adsorbate interaction on the adsorption process. Empirical data of binary adsorption involving doxycycline (DXC) and tetracycline (TTC) were used to validate the improved models. The results indicate that the adjusted pseudo-first-order and pseudo-second-order kinetic models effectively depict the adsorption kinetics when the values approach unity. The competition factor, denoted by the parameter , quantifies the degree to which one adsorbate affects the adsorption capacity of the other. This study indicates that the revised models offer a more thorough understanding of adsorption kinetics in multicomponent systems, thereby facilitating more effective selection of adsorbents and optimization of the process.

Introduction

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A critical consideration in designing an efficient sorption system is accurately assessing the rate at which pollutants are effectively eliminated within a specific solid/solution system. Considerable efforts have been undertaken to develop comprehensive mathematical equations that may effectively elucidate the kinetics of sorption in these systems. The advances in this domain seem constrained by the complexity involved in accurately articulating sorption kinetics, surpassing the theoretical depiction of sorption equilibria. The emergence of this phenomenon can be attributed to the recognition that the governing equations for thermodynamic quantities at equilibrium are fundamentally functional representations of the equations that account for the temporal changes in these values under non-equilibrium circumstances [1].

Some models for interfacial kinetics, like the Langmuir model, can be used as a starting point for making a good model, which can then be changed to fit a specific sorption system. This methodology has resulted in formulating numerous expressions on the phenomenon of a chemical reaction on a surface and its structural arrangement [1]. The predominant interfacial kinetic adsorption models employed to simulate adsorption kinetics in liquid systems are pseudo-first-order and pseudo-secondorder [2-8].

The pseudo-first-order equation, commonly called the Lagergren equation, is widely recognized as an elementary model equation that provides insight into the sorption rate in liquid-phase systems.

Furthermore, this equation has been extensively employed in the field of kinetic adsorption until the present time [1]. The phenomenon known as pseudo-second-order kinetics is commonly observed in cases where the pace of the direct adsorption/desorption process, which can be viewed as a chemical reaction, governs the overall sorption kinetics. Blanchard et al. [9] originally introduced the mathematical equation associated with this model to elucidate the kinetics of heavy metal elimination by utilizing natural zeolites.

The applicability of all adsorption kinetic models derived from interfacial kinetics extends to single-component adsorption systems. Typically, the adsorption process does not include a single component in real conditions. Instead, practically all adsorption processes in real conditions involve the adsorption of many adsorbate components. Up to this point, developing adsorption kinetic models to represent multicomponent systems has remained extremely rare, particularly for pseudo-first-order and pseudo-second-order kinetic models. Thus, this work presents a straightforward adjustment to the pseudo-first-order and pseudo-second-order adsorption kinetic equations to accurately describe the adsorption kinetic data of binary component systems. The adsorbent employed was the metal-organic framework HKUST-1 to accomplish this objective, whereas the binary system adsorbate consisted of the antibiotics tetracycline and doxycycline.

The rationale for choosing tetracycline and doxycycline as adsorbates is their extensive application in treating infections caused by diverse bacterial strains. Nonetheless, both antibiotics are challenging for the body to absorb during metabolism, resulting in substantial excretion into the environment. Moreover, both antibiotics are not readily degradable in the environment. During the preliminary assessment, HKUST utilized in this work showed a significant capacity to adsorb both antibiotics, with HKUST-1 serving as the adsorbent and tetracycline and doxycycline as the adsorbates.

Materials and Methods

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Materials. The raw materials used for the synthesis of HKUST-1 were (Merck, puriss. p.a., 99-104%), trimesic acid/benzene-1,3,5-tricarboxylic acid/H3BTC (Merck, 95%), reverseosmosis water, ethanol ( , ACS reagent) and NaOH (Merck, reagent grade, , pellets (anhydrous)). Meanwhile, the antibiotics used as adsorbates are tetracycline hydrochloride (Merck, (European Pharmacopoeia HPLC assay)) and doxycycline (Merck).

Methods

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Synthesis of MOF HKUST-1. The metal-organic framework HKUST-1 was synthesized at with the following procedure [10]: The first step involved weighing 0.96 grams of particles and dispersing them in 100 milliliters of deionized water. The solution was alkalized to a pH of around 13 by adding 1 M NaOH . Once NaOH was added, the solution was agitated at ambient temperature for 8 hours, forming blackish-green precipitates. Next, the nanoparticles were isolated from the reaction mixture by centrifugation, rinsed with deionized water until the pH reached neutral (6-7), and dried in the oven. The dispersion of the dry nanoparticles ( 0.46 g ) in 230 mL of deionized water was achieved by ultrasonication. An additional 230 mL of a 20 mM ethanol solution of was introduced into the suspension and agitated at ambient temperature for 2 hours. The precipitated products were obtained by centrifugation and rinsed repeatedly with deionized water and ethanol. The resultant product was ultimately desiccated using vacuum pressure.

Characterization of MOF HKUST-1. XRD, nitrogen sorption, and scanning electron microscopy (SEM) techniques were employed to analyze the crystalline structure, pore structure, and surface topography of HKUST-1. The X-ray diffraction (XRD) pattern of the Metal-Organic Framework (MOF) was obtained using a PANalytical X'Pert Pro X-ray diffractometer fitted with a radiation source with a wavelength of 0.15406 nm . The material's pore structure was determined using the Micromeritics ASAP 2020 sorption analyzer. The scanning electron microscopy (SEM) image of MOF HKUST-1 was obtained using the JSM-6390 field emission SEM from JEOL, Ltd., Japan.

Determination of antibiotics concentration. Post-experiment, the initial and residual antibiotic concentrations of the solution being studied were quantified using a UV-Vis spectrophotometer
(SHIMADZU UV/VIS-1700 PharmaSpec) at peak wavelengths of 276.5 nm for tetracycline and 373.5 nm for doxycycline [10].

Adsorption kinetics. The kinetics of adsorption for tetracycline and doxycycline in a binary system using HKUST-1 adsorbent were investigated by the following method: Eight Erlenmeyer flasks were filled with a solution containing tetracycline and doxycycline, each at a concentration of 200 ppm . Each flask was filled with 50 mL of antibiotic solution. Then, 50 mg of HKUST-1 was added to each flask, which was immersed in a shaking water bath and agitated at 200 revolutions per minute. One Erlenmeyer flask was collected at specific intervals to examine the residual tetracycline and doxycycline levels following the adsorption procedure. The amount of tetracycline ( ) and doxycycline ( ) adsorbed by HKUST-1 at time are given by Eq. 1 and Eq. 2, respectively.

Where is the mass of HKUST-1, and is the volume of the antibiotics mixture. The symbols , and represent the initial concentration of tetracycline and doxycycline, respectively. The concentration of tetracycline and doxycycline during the adsorption process at time is given by the symbols , and , respectively.

Results and Discussions

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Characterization of HKUST-1. Fig. 1 displays the surface topography and form of HKUST-1, which was synthesized from and at ambient thermal conditions. An octahedral shape with well-visible edges characterizes the HKUST-1 structure. The particles produced from HKUST-1 exhibit a morphology that closely resembles that of HKUST-1 synthesized using as a metal source [10].

Fig. 1. SEM image of HKUST-1 synthesized from and under alkaline condition

Fig. 1. SEM image of HKUST-1 synthesized from and under alkaline condition

HKUST-1's crystallinity was determined using X-ray diffraction (XRD) analysis. The diffraction profile of HKUST-1 is depicted in Figure 2. Sharp peaks in X-ray diffraction (XRD) patterns indicate a significant level of crystallinity in HKUST-1 obtained from and . From Fig. 2, it can be seen that diffraction peaks of HKUST-1 are observed at of ,
, and . The diffraction peaks correspond to the diffraction indices: , 751, 773, and 882. The X-ray diffraction (XRD) results obtained in this work are consistent with the XRD results of HKUST-1 obtained by Laysandra et al. [10]. The synthesis of HKUST-1 was achieved by employing as a metal cluster and H3BTC as an organic ligand. The analysis suggests that the Cu salt employed has minimal impact on the crystal structure or crystallinity of the HKUST-1 produced. The particle size of HKUST-1 obtained is in the range of .

Fig. 2. XRD diffractogram of HKUST-1 synthesized from and

Fig. 2. XRD diffractogram of HKUST-1 synthesized from and

The measurement of nitrogen gas adsorption and desorption curves in HKUST-1 was conducted at a nitrogen boiling temperature of . Figure 3 presents the obtained results. The presented Figure provides clear evidence that HKUST-1, as obtained in this study, displays both micropores (indicated by significantly high nitrogen adsorption at low relative pressure (< 0.05 )) and mesopores (characterized by consistent nitrogen adsorption at moderate pressure, followed by a rapid rise in adsorption at relatively high pressure). The BET surface area of HKUST-01 was determined by applying the BET equation throughout the relative pressure range of . The resulting value was . HKUST-01, with its large BET surface area, is particularly well-suited as an adsorbent for removing dangerous chemicals from aqueous environments.

Fig. 3. Nitrogen sorption isotherm of HKUST-1 synthesized from and

Fig. 3. Nitrogen sorption isotherm of HKUST-1 synthesized from and

The pore size distribution of HKUST-1 was obtained by applying the NL-DFT equation to nitrogen adsorption data, and the results are given in Fig. 4. HKUST-1 possesses both micropores and mesopores as indicated by the NL-DFT pore size distribution (Fig. 4). The sizes of micropores are 0.4 to 2 nm , while the mesopores mostly between the range 2 to 5.2 nm . Larger sizes of micropores are also observed, but only in a small fraction.

Fig. 4. DFT pore size distribution of HKUST-1 synthesized from and

Fig. 4. DFT pore size distribution of HKUST-1 synthesized from and

Adsorption kinetics. In the late 19th century, Lagergren introduced the empirical rate equation by which oxalic and malonic acids are adsorbed onto charcoal [1]. The pseudo-first-order equation, often known as the Lagergren equation, is among the oldest known equations describing the sorption rate in liquid-phase media. Furthermore, it remains one of the most extensively employed kinetic equations. The Pseudo-first equation has the mathematical expression as follows:

Integration Eq. 3 gives the following result:

We define as the quantity of adsorbate adsorbed by the adsorbent under equilibrium circumstances and as the quantity adsorbed by the adsorbent at time . Parameter is defined as a time constant [1]. To find the values of and parameters, the well-established linear regression approach, based on the linear version of Equation 4, is used. The parameter is a time-scaling factor determining the rate at which a system can achieve equilibrium. A higher value indicates a shorter time required for a system to achieve equilibrium. In several adsorption systems, the pseudo-first-order performance is less favorable than that of the pseudo-second-order [11-18].

Typically, pseudo-second-order kinetics refers to the scenario where the direct adsorption/desorption rate, considered a chemical reaction, governs the total sorption kinetics. The mathematical formulation associated with this model was introduced explicitly by Blanchard et al. [9] to elucidate the kinetics of heavy metal elimination by natural zeolites. The assumption was made that the ion exchange reaction rate on the surface determines the removal kinetics. It was also assumed that the kinetic order of this reaction is 2 with the number of adsorption sites available for the exchange [1]. The differential form of pseudo-second-order kinetic is as follows:

Parameter is a pseudo-second-order constant, usually obtained through linear or non-linear regression of the experimental data. Several investigations and the widespread application of the pseudo-second-order model have demonstrated that specific operating conditions significantly influence the value of . The value of the constant varies significantly with the initial solute concentration applied. Reducing with rising initial concentration is a well-established fact in interpreting as a time-scaling factor. Specifically, a longer time is needed to attain equilibrium when the starting concentration value is higher. Integration of Eq. 5 gives the result as follows:

In the adsorption methodology for treating industrial wastewater, the constituents present in the wastewater consist of a diverse combination of different components. Hence, the adsorption equations, encompassing both kinetics and equilibrium, designed for individual components generally lack applicability in representing the kinetics or equilibrium of adsorption for binary or more components. Therefore, it is necessary to modify or develop the existing adsorption kinetic or equilibrium equations so that they can represent multicomponent adsorption data.

Extensive adjustments have been made to adsorption isotherm equations to accurately characterize multicomponent adsorption systems [10, 19-23]. However, development or modification to adsorption kinetics for multicomponent systems remains uncommon. The process phenomena in the binary system adsorption process are more complicated than those in a single-component process. Competition for the active surface occurs between adsorbate A and adsorbate B and between adsorbate A and the solvent and adsorbate B and the solvent. Furthermore, there is contact between adsorbates A and B among the competition on the surface of the adsorbent. The existence of competitive elements on the active surface and the interaction between adsorbates results in a reduced quantity of adsorbate trapped on the adsorbent compared to the adsorption process involving a single component.

To consider the phenomenon of competitive adsorption between tetracycline and doxycycline included in the solution, competition factors and ( T is tetracycline and D is doxycycline) were introduced, modifying the equilibrium adsorption capacities as follows:

Substitution Eqs. 7 and 8 into Eq. 4 give the following pseudo-first-order kinetic for the binary adsorption process:

For the pseudo-second-order model, the inclusion of competition factors and are conducted in Eq. 5 as follows:

Integration Eqs. 11 and 12 resulting the following equations

The modified models were validated by fitting experimental data of binary adsorption of doxycycline (D) and tetracycline (T). The parameters of Eqs. 9, 10, 13, and 14 were obtained by fitting experimental adsorption kinetics. The fitting process was carried out simultaneously using Eqs. (9) and (10) or (13) and (14) on the binary adsorption data of tetracycline and doxycycline.

Table 1 presents the fitting results for the modified pseudo-first-order and pseudo-second-order equations. Both models demonstrate high conformity to the experimental data, with values approaching 1. Figure 5 presents the theoretical plots and experimental kinetic data for the binary adsorption of tetracycline and doxycycline on HKUST-1 gel.

Table 1. Parameters of modified pseudo-first-order and pseudo-second-order for adsorption of tetracyclin and doxycyclin on HKUST-1.

Modified Pseudo First Order | qe (mg g⁻¹) | k₁ (g mg⁻¹ min⁻¹) | α | R²
Doxycyclin | -5.0536 | 0.1447 | 1.3511 | 0.9960
Tetracyclin | -0.4280 | 0.1843 | 0.1318 | 0.9979
Modified Pseudo Second Order | qe (mg g⁻¹) | k₂ (g mg⁻¹ min⁻¹) | α | R²
Doxycyclin | 132.7106 | 0.0025 | 0.0070 | 0.9976
Tetracyclin | 130.5425 | 0.0026 | 0.0714 | 0.9865

Fig. 5. Binary adsorption experimental data of tetracyclin and doxycycline on HKUST-1 and theoretical values calculated using modified pseudo-first-order and pseudo-second-order

Fig. 5. Binary adsorption experimental data of tetracyclin and doxycycline on HKUST-1 and theoretical values calculated using modified pseudo-first-order and pseudo-second-order

Upon a cursory examination of Fig. 5 and the values indicated in Table 1, it is evident that the equations derived from the pseudo-first-order and pseudo-second-order modifications effectively depict the adsorption data of the binary compounds tetracycline and doxycycline on HKUST-1. To assess the suitability of an adsorption equation in representing the adsorption experimental data, it is essential to evaluate the experimental data fitting results' parameters thoroughly. This evaluation should not rely simply on the value or the visual manifestation of the theoretical results. The values of the parameters obtained must have physical meaning and be reasonable. Upon further examination of Table 1, it is evident that the parameter for the pseudo-first-order modification is negative for both combinations of antibiotics. The qe parameter provides the equilibrium state of an adsorption system and is required to be positive. A negative value obtained from the fitting results of the experimental data suggests that the pseudo-first-order modification is inadequate in representing the experimental data of the adsorption of the tetracycline and doxycycline binary system on HKUST-1.

The competition factors, and , were analyzed to determine the impact of one adsorbate on the adsorption of the other. Values of or greater than 1 suggest a significant competitive effect, while values less than 1 indicate a relatively small impact. If the competition factor approaches zero, it implies that the adsorption of one adsorbate is nearly unaffected by the presence of the other, indicating weak interaction or adsorption at different sites. The competition parameters for tetracycline and doxycycline listed in Table 1 are relatively low, with values of 0.007 for doxycycline and 0.0714 for tetracycline. The low competition parameter values observed for both antibiotics suggest a lack of competition between them for the active group of HKUST-1.

Doxycycline is a tetracycline antibiotic that shares identical physiological characteristics with other tetracycline antibiotics. Both antibiotics possess three pKas, for which their values are nearly identical. Tetracycline has (acidic, tricarbonyl methane moiety), (slightly alkaline, phenoldiketone moiety), and (strong alkaline, ammonium ion moiety). Doxycycline has (acidic, tricarbonyl methane moiety), (slightly alkaline, phenoldiketone moiety), and (strong alkaline, ammonium ion moiety). Closely matched pKa values diminish competition for active adsorption groups for both antibiotics.

Conclusions

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The proposed modifications to the pseudo-first-order and pseudo-second-order kinetic models effectively integrate the phenomenon of adsorbate competition within a multicomponent system. The revised models offer a more comprehensive understanding of adsorption and exhibit high precision in forecasting adsorption kinetics, as indicated by values closely approaching unity. Their implications for designing and optimizing adsorption systems in industrial applications, where multicomponent adsorption is prevalent, are substantial. It is recommended that future studies should
prioritize the investigation of the suitability of these modified models for other systems and enhance the competition parameters to accommodate more intricate situations.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/EC.8.31.pdf

This research was funded by the Directorate of Research, Technology and Community Service, Directorate General of Higher Education, Research and Technology, Ministry of Education, Culture, Research and Technology with contract no. 577A/WM01.5/N/2024.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-chemistry-vol-8/978-3-0364-1706-6

Engineering Chemistry, ISSN: 2813-6535, Vol. 8, pp 41-55

doi: 10.4028/p-Nj6m8O

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-10-01

Revised:
2024-11-05

Accepted:
2024-11-07

Online:
2024-12-13

Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

The original version of this paper is available on https://www.scientific.net/EC.8.41.pdf

Glenn Ongayo and Phoebe Ann Magallanes

¹Department of Chemistry, University of Science and Technology of Southern Philippines, Cagayan de Oro City, 9000 Philippines

gdongayo@gmail.com, phoebeann.magallanes@gmail.com

Keywords: Adsorption, adsorption isotherm, adsorption kinetics, copper ions, graphene oxide

Abstract

Graphene oxide has drawn attention globally as it emerged as a promising adsorbent material with enhanced adsorption of environmental pollutants due to its eco-friendly attributes, high surface area, and cost-effectiveness for mass production. This economical solution is a promising and potentially transformative approach to heavy metal removal, contributing to a cleaner and more sustainable future. In this study, unmodified and modified graphene oxide were examined for the removal of copper (II) ions in an aqueous solution. Modified Hummer's method was utilized to synthesize the graphene oxide. The synthesized graphene oxide was then modified with N trimethoxysilylpropylethylenediaminetriaceticacid (EDTA-silane), resulting in EDTA-modified graphene oxide (EGO). Batch adsorption tests were done for both adsorbents in order to determine the effects of various factors, such as pH , adsorbent dosage, and contact time. Additionally, in order to describe the adsorption behavior of the adsorption system, it was further fitted to isotherm and kinetic adsorption models. Results of adsorption study showed optimum adsorption for copper (II) ions was achieved at , contact time , and adsorbent dosage of 5 mg and 4 mg of unmodified graphene oxide (GO) and modified graphene oxide (EGO), respectively. The fundamental mechanism of both adsorbents was best explained by Langmuir isotherm model and the pseudo-second-order model, indicating that the adsorption system followed chemisorption. The adsorption capacity and maximum removal of copper (II) ions was and for GO, and and for EGO. The latter suggested that graphene oxide treated with EDTA-silane (EGO) has the higher capacity to adsorb copper (II) ions.

Introduction

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Water is necessary and important to human existence. A growing environmental concern on a global scale is water pollution caused by the indiscriminate disposal of metal ions and organic contaminants. Wastewater on various industries, including mining, batteries, metallurgical and chemical manufacturing, contains at least one harmful metal ion. Heavy metal contamination, such as nickel, cadmium, chromium, copper, mercury, lead, and arsenic, has negatively impacted water quality as a result of the demands of urbanization and industrialization [1]. Copper heavy metals are the third most commonly used industrial metal. Due to its increasing accumulation in organisms, this heavy metal is regarded as an eco-toxicological hazard [2]. Metal cleaning smelting, cardboard processing, paints, pigments, mining, fertilizers, electroplating baths, petroleum refining rinses for brass, wood pulp, printed circuit board production, and etching all produce significant quantities of copper metal ions in industrial wastewater [3]. According to studies, drinking water with 30 g of copper ions is fatal, and of copper ions might result in diarrhea, stomach pains, and nausea, especially in infants [4]. According to the WHO and EPA standard reports, copper ions are extremely toxic even at low concentrations. The maximum allowable discharge of polluted copper
ions in surface and well water for industry is , while the maximum allowable copper ion concentration values for drinking water are 0.2 and , respectively [5].
Chemical oxidation or reduction [6], ion exchange, electrochemical treatment, flotation membranes, desalination, chemical precipitation, membrane filtration, extraction, and adsorption have all been developed to remove Cu (II) ions from aqueous solutions. Adsorption is also used to remove heavy metals from wastewater. When applied to the treatment of wastewater, the majority of these techniques have some drawbacks and limitations. Desalination and electrochemical treatments, on the other hand, require relatively high operating costs, and precipitation is inefficient because it produces large quantities of sludge that require careful management [7]. Adsorption is the most common method because of its simplicity, adaptability, resistance to toxic substances, and high efficiency in large-scale applications. The main drawback of the adsorption method is the high cost of effective adsorbents, which makes wastewater treatment more expensive [8]. As a result, removing heavy metals from wastewater requires finding adsorbents that are both effective and cost-effective. Poor adsorption sites, low selectivity, and poor regeneration limit the practical applications of traditional adsorbents like activated carbon, clay, chitosan, magnetic sorbents, resins, silica, zeolites, metal oxides, and alginate in real processes. Thus, it would be beneficial to chemically modify conventional adsorbents to improve their performance by introducing particular functional groups [9].
Graphene oxide (GO) has attracted more attention as an effective adsorbent of dyes and heavy metal ions among the new carbon-based adsorbents due to its good dispersion in water, biocompatibility, and relatively simple and inexpensive preparation methods [10]. Improved hydrophilicity and enhanced interfacial interaction with adsorbates (metal ions and ionic dyes) are the outcomes of the oxygen-containing functional groups in GO, such as carboxyl groups at the edges and hydroxyl and epoxy groups on the basic plane. Metal complexes can be formed by donating a single pair of electrons from these functional groups to the empty orbitals of transition metal ions. According to the theory of hard and soft acids and bases (HSAB), these groups cannot interact strongly with heavy metals like Cu (II), but they can offer a unique opportunity to chemically modify GO. Therefore, the removal efficiency of GO-based materials can be significantly improved by functionalizing GO with molecules that contain strong chelating groups like nitrogen and thiol [11]. Additionally, the large specific surface area of GO offers an abundance of attachment sites for the functionalization of other substances, including ethylenediaminetetraacetic acid (EDTA). As a result, there may be more surface functional groups, which might improve the adsorption of heavy metals. EDTA is an effective chelating agent that, due to its two amine and four carboxylate groups, which can form stable complexes with metal ions [12]. EDTA has several uses, including as a dyeing auxiliary, stabilizer, softener, and in coordination titration [13]. In prior studies, EDTA chelation with divalent metals has been observed, in which one carboxyl group is freely accessible and one water molecule is coordinated to the metal center. Consequently, EDTA is a promising choice for the adsorption of heavy metals and can be functionalized with other materials such as GO. EDTA groups can be simply immobilized on hydroxyl groups of GO by silanization reaction with N-(trimethoxysilylpropyl)ethylenediaminetriaceticacid (EDTA-silane). Several nanomaterials, functionalized with EDTA, have been described and they demonstrated substantial adsorption tendencies towards divalent heavy metals such as Co, Hg , and Cu in aqueous solution [14].
In this study, graphite powder was used to synthesize graphene oxide. The graphene oxide was then modified using EDTA-silane. The general aim of this study was to determine the adsorption efficiency of EDTA-modified graphene oxide for the removal of Copper (II) ions from aqueous
solutions. Specifically, it aimed to: (1) synthesize graphene oxide (GO) from graphite powder; (2) modify graphene oxide using EDTA-silane; (3) determine optimum parameters, such as pH , adsorbent dosage, and contact time required in the removal of Cu (II) ions in aqueous solution using GO and EGO as adsorbents; (4) determine the adsorption capacity and % removal of Cu (II) ions using GO and EGO as adsorbents at optimum conditions; and (5) describe the adsorption behavior of GO and EGO using adsorption studies, in particular isotherms and kinetics for the removal of Cu(II) ions using UV/Vis spectrophotometry analysis after various adsorption experiments.

Methodology

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Synthesis of Graphene Oxide (GO). Graphene Oxide was synthesized according to the modified Hummer's method as described by Paulchamy et al. (2015) [15]. The analytical-grade reagents used in this experiment were sourced from manufacturers located abroad and supplied by a distributor based in the Philippines. A mass of 4 g of graphite powder and 4 g sodium nitrate were mixed in 180 mL of sulfuric acid kept under ice bath to maintain the temperature at with continuous stirring. The mixture was stirred for 4 hours at this temperature and 24 g of potassium permanganate was added to the suspension very slowly. The rate of addition was carefully controlled to keep the reaction temperature lower than . The mixture was diluted with very slow addition of 368 mL deionized water and kept under stirring for 2 hours. The ice bath was then removed, and the mixture was stirred at for 2 hours. The above mixture was kept in a reflux system at for minutes. After 10 minutes, the temperature was changed to which gives brown colored solution. After another 10 minutes, the temperature was changed to , and maintained for 2 hours. The solution finally was then treated with of 40 mL hydrogen peroxide. Deionized water ( 200 mL ) was placed in two separate beakers and an equal amount of 300 mL of solution prepared was added to each beaker and stirred for 1 hour. It was kept for hours without stirring, to allow the particles to settle. The resulting mixture was washed repeatedly by centrifugation at 4000 rpm for 8 minutes with hydrochloric acid and then with deionized water several times until it formed a gel-like substance ( pH -neutral). After centrifugation, the gel-like substance was oven-dried at for 24 hours. Graphene oxide was then stored at room temperature for further adsorption experiments.

Surface Modification of Graphene Oxide. Graphene Oxide was modified with EDTA-silane following the procedure conducted by Jeon and Kim, (2022) [16]. For reaction of GO with EDTA, 1 g of GO was dispersed in 50 mL deionized water and 400 mL of EDTA-silane ( ) was added to this suspension. Then this mixture was stirred for 12 h at . Finally, the product was purified with repeated washing with deionized water. The produced solution was dehydrated in an oven for 13 h at . The final product was called EGO and was used for adsorption studies.

Characterization of the Adsorbents. The samples were delivered to the Central Mindanao University in Maramag, Bukidnon, Philippines and was examined using Fourier Transform Infrared Spectrometer (Shimadzu IR Affinity-1S) for FTIR analysis with a transmission accessory to assess the potential role of the functional groups on the surface of both the GO and EGO in Cu (II) adsorption.

Adsorption Experiments for (II) Adsorption. To produce the 100 ppm stock Cu (II) solution, 0.268 g of copper(II) chloride dihydrate was dissolved in 1000 mL of deionized water. In the batch experiments, both GO and EGO adsorbents were introduced to the copper (II) ion solution under optimal conditions. The effect of , adsorbent dosage ( ), and contact time (15-75 min) were some of these parameters. Every experiment was conducted with a known initial concentration of in a 25 mL Cu (II) ion solution. Either NaOH or were added to
the mixture to modify the pH . The initial concentration and contact time were varied from to and from 15 min to 75 min , respectively, for the isotherm and kinetic investigations.

Analytical Methods. After all the batch adsorption experiments, the UV-Vis Spectrophotometer (Biobase BK-D560 Double Beam Scanning UV/VIS Spectrophotometer) was used to examine the remaining Cu (II) in the filtrates added with concentrated Ammonia solution to form a colored- complex that adsorbed at the wavelength . The concentration of retained Cu (II) ions in the adsorbent phase was used to calculate the adsorption capacity. The ions adsorption capacity ( ) and removal efficiency (%) were calculated according to the following equations:

where and are the initial and equilibrium concentrations ( ) of ions respectively, V is the volume of the metal ion solution in and m is the amount of the adsorbent used in grams .

To ascertain the effectiveness and identify significant differences between the factors applied to the adsorbent, the kinetics and equilibrium adsorption of the GO and EGO was calculated.

The adsorption isotherm model of graphene oxides towards Cu (II) ions was examined by plotting data fit to two isotherm models namely Langmuir isotherm model and Freundlich isotherm model. The monolayer type of adsorption is basically described by the Langmuir model. The availability of interpretable parameters is the key to its advantages. However, because it is structured, its application is restricted to monolayer adsorption. Adsorption Isotherms will be represented by the expression described by Dąbrowski (2001) [17] as shown:

where represent the Langmuir equilibrium adsorption constant and is the Langmuir maximum adsorption capacity ( ). These two parameters of the Langmuir isotherm can be calculated using the slope and the intercept of the plot vs from the equation.

Similar to the Langmuir model, the Freundlich model is also an empirical equation. It is used to estimate the adsorption intensity of the sorbent towards the adsorbate that can be used for multilayer adsorption on heterogeneous sites. It assumes that the adsorption heat distribution and affinities toward the heterogeneous surface are non-uniform. The linear form of the Freundlich equation can be expressed in the following equation as described by Boparai et al. (2011) [18]:

where is described as the adsorption intensity and is Freundlich adsorption constant related to adsorption capacity ( ). Moreover, the slope and intercept of the plot from the equation can also be used to calculate both of these parameters.

Moreover, the following equations were used to calculate pseudo first-order and pseudo second-order, respectively, using the experiment data on the effect of contact time:

Results and Discussion

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Adsorbent Structural Characterization. To provide a substantial approximation to the nature of the synthesized graphene oxide, both the unmodified and modified graphene oxide underwent a Fourier Transform-Infrared Spectroscopy (FTIR). The spectra of the adsorbents were measured in the range of . The distinctive absorptions of GO (highlighted in yellow) and EGO (highlighted in orange), as well as the raw graphite (highlighted in blue) were depicted in Fig. 1, while Table 1 provides a summary of the significant peaks alongside their corresponding functional groups.

Table 1. Characterization of GO and EGO using FTIR Analysis.

Adsorbent | Band Positions (cm⁻¹) | Assigned Functional Group
GO | 3380-3390 | O-H stretching
| 1790-1800 | C=O stretching
| 1610-1625 | C=C stretching
| 1050-1080 | C-O stretching
EGO | 3400-3500 | O-H stretching
| 1700-1730 | C=O stretching
| 1605-1615 | C=C stretching
| 1125-1250 | C-N stretching
| 900-925 | Si-O stretching

Figure 1 shows the spectra of the raw graphite, and both modified and unmodified graphene oxide, wherein there is a noticeably higher % transmittance for the modified graphene oxide compared to the unmodified graphene oxide - which could be representative of the unmodified graphene oxide and its hygroscopic nature [19].

Fig. 1. Infrared spectra of unmodified and modified synthesized graphene oxide (Orange, EGO; Yellow, GO; Blue, Graphite)

Fig. 1. Infrared spectra of unmodified and modified synthesized graphene oxide (Orange, EGO; Yellow, GO; Blue, Graphite)

The absence of any significant peak in the graphite FTIR spectrum suggests that bulk graphite is chemically inert [20]. However, different functional groups were visible in the FTIR spectra of the modified and unmodified graphene oxide. Furthermore, the graph shows a broad peak of hydroxyl group (-OH) at where it fell between the range of , indicating the presence of absorbed water molecules on the surface of GO. Therefore, it shows that this GO sample has strong hydrophilicity. The absorption peak at represents the carbonyl group ( ) where it fell between the range of 1790-1800 that relates to carboxylic acid and its existence is at the edge of GO. The absorption peak of vibration at from the bonds of the aromatic rings of GO, as well as from possible scissoring mode of the water present in the samples represents the graphitic domain where the range is between . It indicates the exfoliation happened where oxygen had been introduced during oxidation process thus contributes to the hydrophilic nature of GO sample [21]. Finally, the peak absorption at corresponds to the presence of epoxy group ( ), which lies between the range of 1050-1080 [22].

After the surface functionalization, the stretching, stretching, and stretching peaks at 3464,1720 , and , respectively, were not significantly affected, but stretching peaks were diminished with appearance of new peaks at 1128 and from and stretching due to functionalization with the amine and alkoxysilyl groups in EDTA- silane, respectively [23]. These changes indicated that EDTA-silane was successfully immobilized onto the surface of GO through the reaction between hydroxyl groups of GO and N-(trimethoxysilyl) group of EDTA-silane.

Batch Adsorption Studies. To profile the adsorption capabilities of both the GO and EGO, three parameters, namely: (1) pH , (2) adsorbent dosage, and (3) contact time, served as experimental parameters for the batch adsorption experiments. The correlation between pH , adsorbent dosage, and contact time is crucial in understanding the efficiency of adsorption processes. Optimal pH enhances adsorption efficiency based on surface charge interactions. Higher adsorbent dosages can improve removal rates until saturation is reached. Sufficient contact time is necessary to achieve equilibrium, with optimal times depending on the other two factors. Other variables such as temperature ( 298 K ), initial concentration ( 25 ppm ), volume ( 25 mL ), and the rate of mechanical stirring ( 300 rpm ) are consistent unless otherwise stated.

Effect of pH. The effect of pH on the adsorption of ions using GO and EGO is shown in Fig. 2.

Fig. 2. Effect of pH towards adsorption of Cu (II) ions by GO and EGO

Fig. 2. Effect of pH towards adsorption of Cu (II) ions by GO and EGO

The pH value of the solution is one of the most significant factors that control the adsorption of heavy metal ions to solid particles, which could have an impact on the surface potential characteristics of adsorbent and the relative distribution of metal ion species, particularly the protonation-deprotonation reactions at various pH levels. In this study, pH was varied at a range of 3 to 7. The results demonstrated that the percentage removal of GO-based materials for Cu (II) is pH responsive in which when pH was raised to 7 , the percentage of removed by GO and EGO also increased steadily, reaching a maximum value of and , respectively. Furthermore, the adsorption capacity of GO and EGO was and 197.06, respectively. Graphene oxide (GO) effectively removes pH from solutions through various mechanisms. Its high surface area and functional groups, such as hydroxyl, carboxyl, and epoxy, attract and hold onto ions and molecules. GO's ion exchange processes lower pH , while its surface charge changes with the pH , attracting negatively charged species. GO can also participate in chemical reactions with pollutants, such as oxidizing agents or redox reactions, leading to the removal of specific contaminants. GO's ability to induce flocculation and aggregate at higher concentrations further aids in solution treatment. At low pH values, the high concentrations of and in solution would compete with heavy metal ions to attach to the available binding sites of GO and EGO which reduced the adsorption of metal ions at low pH . On the other hand, at high pH levels, the oxygen-containing functional groups on the surfaces of GO and EGO were readily protonated. Metal ion binding can be facilitated by adding more sites and ligands. The enhancement of electrostatic interaction between positively charged metal ions and negatively charged GO and EGO surfaces is a contributing factor to an increase in adsorption capacity seen with increasing solution pH [24]. Moreover, a study by White et al. (2018) stated that precipitation of copper as is more likely to be dominant than ions as . Proving this claim, an additional adsorption at was conducted, and a formation of precipitate was observed having a removal rate of and an adsorption capacity of for GO, and and an adsorption capacity of for EGO. The observed decrease in adsorption after pH 7 can be attributed to the formation of soluble hydroxyl complexes. After pH 7 , there are comparatively tiny quantities of three species: , and , which have a major impact on metal adsorption [25].

Effect of Adsorbent Dosage. Adsorbent dosage is an important consideration for the removal of heavy metal ions. The ability to remove substances from a solution can be influenced by varying the amount of adsorbent, which can impact its overall effectiveness. In this study, the adsorbent dosage was varied from 2 mg to 6 mg . Fig. 3 shows the effect of adsorbent dosage for both GO and EGO on the Cu (II) removal having a substantial and noticeable rise in the adsorption of Cu (II) at 5 mg with and an adsorption capacity of for GO and 4 mg with and an adsorption capacity of for EGO. One could reasonably expect that a higher dosage of adsorbent would result in more exchangeable sites being available for the metal ions. However, after 5 and 4 mg for GO and EGO, respectively, a decrease in the removal percentage was observed. These results can be explained from the perspective of the adsorbent as well as the adsorbate. (1) The absorbent perspective. Adsorption percent increases, when more adsorbates are adsorbed at increasing adsorbent dosages. However, as the dosage of the adsorbent increases, less free adsorbates remain, resulting in a decrease in the amount of adsorption. (2) The absorbent perspective. Increasing the dosage of adsorbent results in an increase in adsorption sites, which in turn adsorbs more free adsorbates and raises the adsorption percent. Adsorbents, however, have the potential to aggregate at large dosages, which would reduce the amount of adsorption [26]. Furthermore, the unmodified GO required a significantly higher adsorbent dosage ( 5 mg ) compared to EGO ( 4 mg ) for optimal copper removal can be due to the EDTA modification of EGO and its pore structure. Introducing
nitrogen-containing functional groups and chelating agents onto the graphene oxide surface provides additional binding sites for copper ions, potentially increasing the adsorption capacity at lower dosages. Modifications can also alter the pore structure of graphene oxide. This might increase the accessibility of internal pores, leading to higher copper uptake at lower dosages [27].

Fig. 3. Effect of adsorbent dosage towards adsorption of Cu (II) ions by GO and EGO.

Fig. 3. Effect of adsorbent dosage towards adsorption of Cu (II) ions by GO and EGO.

Effect of Contact Time. Contact time is one of the critical adsorption parameters as it influences the mass transfer rate of substances. The suitable contact time of each adsorbent depends on the equilibrium point. For majority of metal ions, the removal efficiency initially increases dramatically and then increases slowly until the contact time is extended to reach equilibrium [24]. Regardless of the other experimental parameters, the duration of contact time in an adsorption system has a substantial impact on the adsorption kinetics. For the purposes of this investigation, the adsorption contact time was varied at 15, 30, 45, 60, and 75 minutes. Fig. 4 shows that the contact time for both GO and EGO affect the removal of Cu (II) ions, with a significant and discernible increase in Cu (II) adsorption occurring at 45 minutes is with an adsorption capacity of for GO, and with an adsorption capacity of for EGO. It is clear from the graphical representation that as the contact duration increases from 15 to 45 minutes, the percentage of copper ions removed increases. The initial increase in efficiency could be related to the availability of bindings sites on adsorbent. There is an abundance of adsorption sites available in the initial phase of the adsorption process, which makes it easier for Cu (II) ions to attach to these sites [28]. Because there is less rivalry among the adsorbate molecules for binding sites on the adsorbent surface, this availability promotes a higher rate of adsorption. However, beyond a certain point, an increase in contact time may not result in any further adsorption, indicating that the adsorption process has reached equilibrium. Eventually, all the sites get filled, and no further adsorption can occur, reaching a saturation point [25].

Fig. 4. Effect of Contact Time towards Adsorption of Cu (II) by GO and EGO.

Fig. 4. Effect of Contact Time towards Adsorption of Cu (II) by GO and EGO.

Comparative Analysis of Adsorbents. The data from the adsorption experiments involving different pH levels, varying adsorbent dosages, and different durations of contact time were subjected to the following tests: Two-way Analysis of Variance (ANOVA) and t-tests at a significance level of 0.05 to investigate further into the collected data concerning the adsorption characteristics of both GO and EGO. These experiments sought to compare the adsorption characteristics of both GO and EGO. The information obtained from the experiment measuring adsorption performance provided slopes depicting the rate of Cu (II) ions removal under different conditions, allowing for comparative analysis. Slopes showing the rate of removal of Cu (II) ions under various conditions were acquired from the experiment evaluating adsorption performance, enabling a comparative analysis. Table 2 and Table 3 present the results of the Two-way ANOVA and t-test analysis conducted on both GO and EGO under three different parameters ( pH , adsorbent dosage, and contact time).

Table 2. Two-way Analysis of Variance (ANOVA) for GO vs EGO over % Removal of Cu(II) ions

Conditions of GO vs EGO | F value | F critical
Varying pH | 5 |
Varying Adsorbent dosage | 5.71 | 2.87
Varying Contact Time | 4.20 |

Table 3. t-Test analysis for GO vs EGO over % Removal of Cu(II) ions.

Conditions of GO vs EGO | P value | P critical
Varying pH | 3.21 ×10⁻⁴ |
Varying Adsorbent dosage | 7.96 ×10⁻⁷ | 0.05
Varying Contact Time | 1.64 ×10⁻⁷ |

Table 2 shows the result of the two-way ANOVA analysis, where the percentage removal of Cu (II) ions for both types of adsorbent at varying pH , adsorbent dosage, and contact time are statistically significant. It shows that the calculated F -values were larger than the F -critical value, indicating that the types of adsorbents and the varying parameters used affect the percentage removal of Cu (II) ions in the solution and their means differ significantly from each other. Meanwhile, results in Table 3 describes that in all conditions, EGO have a significant difference ( p value crit) in terms of percent removal towards ions. The positive p values also show that there is a positive significant difference, suggesting that the modification improved the efficiency of Cu removal from the solution by adsorption. Furthermore, it provides more evidence in favor of the notion that adding functional groups from EDTA-silane may improve the ability of graphene oxide to adsorb heavy metal ions like Cu (II) from aqueous solutions.

Adsorption Isotherm and Kinetics. The adsorption behavior of GO and EGO concerning Cu(II) ions were graphically analyzed and fitted to both Langmuir and Freundlich isotherms. The purpose of this analysis was to ascertain if a multilayer adsorption mechanism or chemisorption monolayer adsorption was employed in the interaction between the adsorbate ( ions) and the adsorbent (GO and EGO). When adsorbate molecules form a single layer on the adsorbent surface, the Langmuir model usually implies monolayer adsorption; in contrast, the Freundlich model predicts a multilayer adsorption phenomenon, in which adsorbate molecules create multiple layers or display heterogeneous adsorption behavior [29]. To determine which model best captures the adsorption behavior in this particular system, these different models are compared.

The graphs illustrating the linearized data for Langmuir (Fig. 5a and 5b) and Freundlich (Fig. 5c and 5d) isotherms, indicate that for both GO and EGO, the value associated with Langmuir isotherm model, 0.9780 and 0.9314, respectively are higher that the Freundlich isotherm model, 0.9599 and 0.8782 as shown in Tables 4 and 5. Based on these findings, the Langmuir isotherm model, which describes how the adsorbate forms a monolayer at maximum coverage in certain active sites on the adsorbent surface, best describes the adsorption systems of both GO and EGO. When a binding site is occupied by sorbate, it is assumed that no more adsorption will occur there for a monolayer coverage to exist. As a result, there is a one-to-one ratio between the adsorbate and the adsorbent, and it is unlikely that the adsorbate will stack [30].

Table 4. Langmuir Parameters of GO and EGO towards the Adsorption of Cu (II) ions.

Adsorbent
Langmuir Parameters

qmax
(mg/g)
KL (L/mg)
RL
R₂

GO
672.22
0.01626
0.7110
0.9780

EGO
729.11
0.03370
0.5427
0.9314

Table 5. Freundlich Parameters of GO and EGO towards the Adsorption of Cu (II) ions.

Adsorbent | Freundlich Parameters
| KF | 1/n | R2
GO | 10.5977 | 0.9681 | 0.9599
EGO | 30.3506 | 0.6686 | 0.8782

Furthermore, the results of this study were used to determine the maximum monolayer saturation capacity of two materials, GO and EGO. Based on theoretical calculations, it was found to be for GO and for EGO which indicates that EGO has a significantly greater capacity than GO for molecules to be adsorbed in a single layer on its surface, suggesting that EGO may have a stronger potential for adsorption. Furthermore, the equilibrium factor (RL) of Langmuir isotherm displays a positive value (GO: 0.7110 , EGO: 0.5427 ). The type of isotherm is indicated by the value of RL , which can be either irreversible ( ), favorable ( ), linear ( ), or unfavorable ( ).

The RL values for EGO ( 0.5427 ) and GO ( 0.7110 ) are both significantly below 1 when analyzed in which according to the Langmuir isotherm model, suggests that the adsorption behavior for both materials is favorable. An efficient and desirable adsorption process is indicated by a favorable RL value ( ). Lower RL values (EGO) in this situation imply greater favorability of the adsorption process.

Fig. 5. Adsorption isotherm of Cu (II) ions. (a) Langmuir adsorption isotherm using GO; (b) Langmuir adsorption isotherm using EGO; (c) Freundlich adsorption isotherm using GO; and (d) Freundlich adsorption isotherm using EGO.

Fig. 5. Adsorption isotherm of Cu (II) ions. (a) Langmuir adsorption isotherm using GO; (b) Langmuir adsorption isotherm using EGO; (c) Freundlich adsorption isotherm using GO; and (d) Freundlich adsorption isotherm using EGO.

To ascertain the characteristics of adsorption (either physisorption or chemisorption) of GO and EGO towards the removal of Cu (II) ions, this study examined both the pseudo-first-order kinetics by Rudzinski and Plazinski (2006) [31] and the pseudo-second-order kinetics developed by Ho and McKay (1999) [32]. Since the longest adsorption exposure of the experiment was obtained during this effect of contact time, data from it was employed (Table 6). These data were graphed and analyzed by applying linearized forms of both the pseudo-first-order (Fig. 6a and 6b) and pseudo-second-order equations (Fig. 6c and 6d).

Table 6. Comparison of Adsorption Kinetics of Both GO and EGO.

Pseudo-First Order
Pseudo-Second Order

q₁
(mg/g)
K₁
R₂
q₂
(mg/g)
K₂
R₂

GO
3.0607
1.02 ×10⁻⁵
0.001
85.71
0.0094
0.9998

EGO
3.1884
5.29 ×10⁻⁵
0.1045
121.59
0.0206
0.9478

Fig. 6. Adsorption kinetics of Cu (II) ions. (a) Pseudo-First order kinetics using GO; (b) Pseudo-First order kinetics using EGO; (c) Pseudo-Second order kinetics using GO; and (d) Pseudo-Second order kinetics using EGO.

Fig. 6. Adsorption kinetics of Cu (II) ions. (a) Pseudo-First order kinetics using GO; (b) Pseudo-First order kinetics using EGO; (c) Pseudo-Second order kinetics using GO; and (d) Pseudo-Second order kinetics using EGO.

As observed from the data in the Figures, the values that came from the pseudo-second-order model ( 0.9998 for GO and 0.9478 for EGO) were significantly higher that the values that came from the pseudo-first-order model. Based on the assumption that chemical sorption or chemisorption is the rate-limiting step, the pseudo-second-order model, which predicts behavior across the entire adsorption range, provides a better explanation for the adsorption kinetics of GO and EGO towards Cu (II) ions. With these results, it is further proven that the GO-Cu(II) and EGO-Cu(II) sorption systems follow chemisorption where chemical interaction occurs between the adsorbate and the functional group present works as binding sites of the surface of the adsorbent [33].

The pseudo-second-order kinetic model has become among the most popular ways to fit rate data for adsorption of metal ions, dyes, and other compounds from aqueous solution [34]. In addition, Wang and colleagues (2013) [35] investigated various parameters, such as pH , adsorbent dosage, contact time, temperature, and coexisting ions, on the adsorption performance of graphene oxide
(GO) for the removal of heavy metals in batch reactions. The findings demonstrated how well the adsorption process fit the pseudo-second-order kinetic model and the Langmuir isotherm, demonstrating the effectiveness of GO as an adsorbent.

Conclusions

The original version of this paper is available on https://www.scientific.net/EC.8.41.pdf

The removal of Cu (II) ions from aqueous solutions was investigated by the use of graphene oxide. The removal efficiency of both GO and EGO in response to copper(II) ions was found to be strongly influenced by variations in pH , adsorbent dosage, and contact time. The removal efficiency of these two adsorbents was found to be limited by achieving a state of equilibrium and the saturation of binding sites. Since the adsorption process of both GO and EGO favors a Langmuir isotherm and a pseudo-second-order model, the removal of Cu (II) ions was best described by chemisorption, thus the formation of an adsorbate monolayer takes place in specific active sites on the surface of GO and EGO. However, following further investigations may be conducted to enhance the current results of the study, to wit: (1) additional parameters be tested, such as agitation speed, ionic strength, point of charge, and temperature in batch adsorption experiments to properly determine the optimal working conditions of the unmodified and modified GO; (2) enhancing the yields of the adsorbents through the use of efficient equipment and standard procedures is also strongly advised for practicability of synthesis; (3) in-depth study of the reusability of EGO should be further explored to confirm the longevity of EGO as an adsorbent. An essential component of assessing the adsorption performance of the material is the number of cycles at which the adsorbent may continue to function. different desorbing or regenerating solvents, like acids, bases, and chloride salts, can be used in these studies looking into the reusability of adsorbent; (4) the potential and versatility of EGO as an adsorbent for various pollutants can also be assessed by removing additional heavy metal ions and organic and/or inorganic compounds; and (5) it is also advised that more replicates be carried out and that more values of pH , contact time, and adsorbent dosage ranges be investigated, to further extrapolate the results and produce effective and reliable estimates to the trend of adsorption. Nevertheless, this study presents a Cu (II) removal by a simple and cost-effective adsorbent. Therefore, for effective copper uptake, chemically produced EGO may be a preferable option than unmodified GO.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EC.8.41.pdf

The authors express their gratitude to M.S. Ceniza, F.N.P. Hamoy, and G.D. Leopoldo for their invaluable inputs throughout this study. Moreover, the authors extend their heartfelt thanks to the Department of Chemistry, the rest of the laboratory technicians, friends and family, for the helpful advice and provision of the researcher's need during the experiments. Above all, to Almighty God for the fortitude to seek knowledge until the very end.

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The original version of this paper is available on https://www.scientific.net/EC.8.41.pdf

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Engineering Chemistry, ISSN: 2813-6535, Vol. 8, pp 57-63

doi: 10.4028/p-Xm0sPv

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-25

Revised:
2024-04-23

Accepted:
2024-04-23

Online:
2024-12-13

Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

The original version of this paper is available on https://www.scientific.net/EC.8.57.pdf

Alvan Ade Reza , Mahidin Mahidin , Yunardi Yunardi , Asri Gani and Sari Wardani

Doctoral School of Engineering, Universitas Syiah Kuala, Aceh, Indonesia

Department of Chemical Engineering, Universitas Syiah Kuala, Aceh, Indonesia

Department of Animal Husbandry, Universitas Abulyatama, Aceh, Indonesia

alvan_ar@mhs.usk.ac.id, mahidin@usk.ac.id, yunardi@unsyiah.ac.id, asri_gani@usk.ac.id, sariwardani_peternakan@abulyatama.ac.id

Keywords: Characteristics Serpentine Aceh, Activated Thermal, SEM-EDS, XRF

Abstract

Carbon dioxide is a dangerous pollutant that harms the environment and triggers global warming, which causes greenhouse gases. Serpentine is a rock rich in magnesium silicate, which can be used to reduce carbon dioxide pollutants through adsorption technology. Aceh Province has the potential for serpentine rocks, which can be processed to adsorb carbon dioxide pollutants. This research examines the characteristics of serpentine based on its morphology and chemical composition as a carbon dioxide adsorbing material. The serpentine aceh was obtained from Indrapuri Regency, Aceh Province. The thermal activation stage is as follows: serpentine aceh is collected, cleaned, reduced in size, and dried, then serpentine aceh is ground to a size of 50 mesh (297-149 mesh and 150 mesh . The serpentine aceh was thermally activated using a furnace at for 1.5 hours for all particle sizes. The characterization of serpentine aceh, which consists of morphological and chemical structure analysis using scanning electron microscopyenergy dispersive spectroscopy (SEM-EDS) and chemical composition using X-ray fluorescence (XRF). The results show that the characteristics of serpentine aceh before being activated have a lizardite and chrysotile structure with a chemical composition of and , and after being thermally activated, serpentine aceh has an antigorite structure with a chemical composition of and at 150 mesh. This research contributes to utilizing one of Aceh's natural mineral resources, serpentine rock, as an alternative CCS material to reduce greenhouse gas emissions.

Introduction

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The province of Aceh is one of the areas with the potential for spreading serpentine rocks, especially in the territory of Aceh Besar District. The spread pattern of the rocks in the Aceh Besar Region forms a hill starting from Kuta Malaka district in the west direction towards the east, then in Indrapuri district to Kuta Cot Glie district [1]. The effect of greenhouse gases causes global warming due to being a pollutant [2]. Increasing concentrations in the atmosphere affect the ability of plants and oceans to assimilate , resulting in being trapped in the earth's atmosphere. This phenomenon causes global warming, indicated by rising sea levels, climate change and increasing global temperatures. Excessive releases from industrial activities, power plants and other sources have an essential influence on the global climate and the earth's life cycle [3]. Serpentine rock is a metamorphic rock formed due to changes in basalt rock on the seabed. Serpentine mineral content belongs to a group of silicate minerals called phyllosilicate. Serpentine is used as decoration and raw material in the mineral industry. Serpentine contains the minerals silica and calcite found in rock cavities [4]-[7]. Serpentine is a non-metallic mineral that is applied in engineering, such as adding building materials, carbon adsorption materials, and utilizing pure magnesium or amorphous silica through the extraction method [8], [9]. The characteristics of serpentine are green, brown, red and black; strength between 2.5-5; orthorhombic, monoclinic and hexagonal crystal forms. Density 2,52,6. Serpentines are derived from ultra-fine rocks that undergo hydrothermal alterations and
alterations of minor elements such as nickel, chromium, cobalt, and scandium. The serpentine base crystal structure is a silicate layer connected with the octahedral layer [10]. The constituent structures of serpentine minerals are antigorite , Chrysotile , Lizardite , Orth chrysotile , Para chrysotile , Magnetite and Amphibole, pyroxene and talc .

Potential large serpentine reserves will be applied in high technology, such as carbon capture and carbon storage processes, as a potential source of raw materials for nickel and magnesium in producing metals. Magnesium carbonate is found in an anhydrous form known as magnesite and is the most thermodynamically stable and efficient carbon storage medium [14]. When heated, serpentine will undergo several reaction stages, namely dehydration, dihydroxylation, and forsterite crystallization at temperatures above . Rock formation processes greatly influence the mineral content of serpentine. The activation process on serpentine aims to remove found on the serpentine surface, which is a barrier to the reaction between MgO and [15]. Thermal and chemical activation of serpentine, where thermal activation uses high temperatures to open the pores of the material by removing chemical bonds or oxidizing elements or compounds found on the surface of the serpentine so that it can affect the adsorption performance of the adsorbent material. Thermal activity is aimed at expanding the volume and diameter of pores and can produce new pores. Several studies have evaluated the impact of particle size during thermal activation, indicating that halved materials provide higher yields. The thermal activation method can be carried out using , , and . The serpentine must be activated first to improve the quality of serpentine as a Carbon Capture and Storage (CCS) material. Several studies show that thermally activated serpentine can increase the Mg content so that the adsorption process can occur optimally [16], [17].

SEM-EDS is an instrument for analyzing microstructures and elements of various kinds of materials. SEM-EDS consists of two devices combined into one. SEM works to produce images of samples from surface scanning using an electron microscope at a certain scale that is magnified. The interaction between electrons and atoms in a sample will provide a signal containing information about the surface morphology as well as the topography and composition of the sample. EDS is used for elemental chemical analysis of materials. Characteristics are assessed based on the uniqueness of the atomic structure of an element that allows a series of peak spectrum to occur in electromagnetic emissions. XRF can analyze various elements, from sodium to uranium in various concentrations from percentage content to parts per million (ppm) in samples in solid or liquid. This analysis technique measures the elemental composition of metals, ceramics, and composite materials. This study compares the characterization of serpentine materials based on their morphology and chemical composition before and after activation. This research contributes to utilizing one of Aceh's natural mineral resources, serpentine rock, as an alternative CCS material to reduce greenhouse gas emissions.

Materials

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The serpentine aceh (SA) was obtained from Indrapuri District, Aceh Province.
Methods. This research was carried out in two stages, namely, the thermal activation stage, which was performed through the following mechanism: Aceh serpentine was collected, cleaned, reduced in size using a ball mill, and dried using sunlight to reduce the water content. Aceh serpentine was ground to 50 mesh mesh , and 150 mesh using a mesh sieve. Aceh serpentine was then thermally activated using a furnace (Thermolyze Fb capacity, 2.1 liters) at a temperature of for 1.5 hours. In the second stage, Aceh serpentine was characterized using scanning electron microscope-energy dispersive spectroscopy (SEM-EDS JEOL JSM-6510LA) to study its morphology and chemical structure and analyzed for its chemical composition using X-ray fluorescence (XRF Rigaku Supermini200). Characteristic testing was carried out on serpentine before and after activation.

Result and Discussion

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The SEM-EDS characterization results of serpentine aceh are shown in Figure 1(a) before activation and Figure 1 (b,c, and d) after activation at sizes 50,100 , and 150 mesh, respectively. In contrast, the EDS results are shown in Figure 2 and Table 1. The results of serpentine morphology analysis before and after thermal activation are very different based on SEM-EDS results. Figure 1 shows the results of SEM analysis of serpentine aceh before and after activation at various particle sizes. From these four images, there has been a change in the morphological structure in images , 1. c., and 1.d (activated serpentine aceh), where it can be seen that the impurity elements that previously covered the serpentine aceh (Figure 1.a) have decreased. In figure 1.a, there are no visible pores, whereas in figure1.b, 1.c. and 1.d the pores are visible. serpentine aceh that has not been activated has a layered structure, while activated serpentine has structural destruction that is shown by the formation of agglomerates. The morphology of serpentine aceh before activation is a chrysotile structure, whereas after thermal activation at a temperature of for 1.5 hours, there is a change in morphology with an antigorite structure. The antigorite structure is a structure that is rich in forsterite compounds . The formation of the forsterite reaction occurs at , and the dihydroxylation of lizardite crystal water occurs gradually under endothermic conditions. Serpentine is heated to temperatures above , producing serpentine rich in magnesium [10]. Other research results state that the three polymorphs of the serpentine group are antigorite, kadalite, and chrysotile. According to a study, lizards are very fine-grainy and flat, chrysotile is fibrous, and antigorite is made up of grooved plates or fibres [18]. According to other studies, chrysotile is a fibrous variant, whereas antigorite and kadalite are thick or fine-grained. In micrographs, serpentinegroup minerals are seen as layers and parallel aggregates with a fibrous arrangement of long rods [19], [20]. Activated serpentine has a larger pore volume than serpentine that has not been activated [16].

Fig. 1. SEM analysis at (a) SA pre activated. (b) SA activated at particle sizes 50 mesh. (c) SA activated at particle sizes 100 mesh. (d) SA activated at particle sizes 150 mesh

Fig. 1. SEM analysis at (a) SA pre activated. (b) SA activated at particle sizes 50 mesh. (c) SA activated at particle sizes 100 mesh. (d) SA activated at particle sizes 150 mesh

Figure 2 and Table 1 show that the magnesium element in serpentine aceh has changed after activation. Before activation, the magnesium element content was ; after activation, at 50 mesh, it was ; at 100 mesh , it was ; and at 150 mesh , it was . This is because in the thermal activation process, the carbon element, one of the largest elements contained in serpentine aceh, is decomposed to increase the magnesium element in the activated serpentine aceh. Serpentine aceh also contains the elements oxygen, silica, aluminum, and sulfur in different percentages (Table 1).

Fig. 2. EDS analysis at (a) SA pre activated (b) SA activated at particle sizes 50 mesh (c) SA activated at particle sizes 100 mesh (d) SA activated at particle sizes 150 mesh

Fig. 2. EDS analysis at (a) SA pre activated (b) SA activated at particle sizes 50 mesh (c) SA activated at particle sizes 100 mesh (d) SA activated at particle sizes 150 mesh

Table 1. SA mineral compound composition pre activated and activated based on EDS analysis

Mineral | SA pre activated (%) | SA activated at particle sizes 50 mesh (%) | SA activated at particle sizes 100 mesh (%) | SA activated at particle sizes 150 mesh (%)
C | 43.6 | 6.8 | 3.4 | 6.5
O | 38.9 | 49.1 | 48.3 | 48.1
Mg | 9.0 | 23.4 | 25.7 | 24.7
Si | 7.0 | 19.7 | 22.6 | 20.7
Al | 0.8 | 1.0 | 0 | 0
S | 0.7 | 0 | 0 | 0

Table 2 shows the mineral composition of serpentine aceh pre activated and activated based on XRF analysis. The composition of serpentine aceh before and after activation shows a very different difference in MgO content. Serpentine aceh contains pre activated, and activated the MgO content increases by and in size particles of 50,100 , and 150 mesh,
respectively. The smaller the particle size of serpentine aceh, the higher the MgO content. MgO is the most important compound in a material that adsorbs because it can react with to make [21], [22]. The majority of serpentine aceh contain high percentages of MgO and . Serpentine aceh also contains other minerals, namely , and .

Table 2. Aceh serpentine compound composition pre activated and activated based on XRF analysis

Mineral | SA pre activated (%) | SA activated at particle sizes 50 mesh (%) | SA activated at particle sizes 100 mesh (%) | SA activated at particle sizes 150 mesh (%)
SiO₂ | 38.83 | 45.41 | 45.28 | 45.42
MgO | 29.95 | 36.63 | 36.78 | 36.83
Fe₂O₃ | 7.35 | 9.06 | 8.58 | 8.72
CaO | 0.85 | 1.21 | 1.05 | 1.04
Al₂O₃ | 0.94 | 1.16 | 0.99 | 1.08
Cr₂O₃ | 0.36 | 0.43 | 0.34 | 0.29
Mn₂O₃ | 0.06 | 0.09 | 0.09 | 0.10
SO₃ | 0.05 | 0.05 | 0.03 | 0.06
Na₂O | 0.04 | 0.03 | 0.01 | 0.03
K₂O | 0.04 | 0.04 | 0.04 | 0.03
TiO₂ | 0.04 | 0.03 | 0.02 | 0.02
P₂O₅ | 0.03 | 0.07 | 0.05 | 0.04
*LOI | 17.11 | 1.69 | 1.85 | 2.34

LOI loss on ignition

Conclusion

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The characteristics of the serpentine aceh have been studied using SEM-EDS and XRF to analyze the morphology and chemical composition of materials. Aceh serpentine has a morphology of chrysotile (pre activated) and antigorite (activated). The three highest compound contents found in aceh serpentine are and . Thermal activation at a temperature of and 1.5 hours can increase the MgO content . The level of MgO content that increases after activation is expected to become an alternative material for the capture and storage of , a source of emissions that cause the greenhouse effect. Although more research needs to be done on adsorption tests to find out how effective and efficient the Aceh serpentine material is, this will help find the best thermal activation treatment in terms of temperature, pressure, and flow rate.

Acknowledgements

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This research was funded by the Directorate of Research, Technology and Community Service. The Directorate General of Higher Education, Research, and Technology Ministry of Education, Culture, Research, and Technology according to the State University Operational Assistance Program Implementation Contract for the 2023 Fiscal Year Research Program No: 168/E5/PG.02.00.PL/2023.

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Engineering Chemistry, ISSN: 2813-6535, Vol. 8, pp 65-73

doi: 10.4028/p-b1UVUg

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-25

Revised:
2024-01-05

Accepted:
2024-02-19

Online:
2024-12-13

Determination of the Optimum Composition of Chitosan-Grafted Cellulose/TiO2 Films Using the Box Behnken Design Method

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Haya Fathana , Surya Lubis , Muhammad Adlim , and Rahmi

Graduate School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh 23111, Indonesia.

Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh, 23111, Aceh, Indonesia.

Department of Chemistry Education, Faculty of Teacher Training and Education, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia.

Abstract

Determination of the optimum composition of chitosan-grafted cellulose film was performed using the Box-Behnken Design method from Response Surface Methodology (RSM). Apart from being able to determine the best composition for a composite, this method can also determine the influencing factors of the composition on the performance of the composite. This method was conducted by combining factorial group design. This design used three factors and three levels. Adsorption capacity data from the adsorption process and percent removal from the photocatalyst process were used as responses in determining the optimum composition of chitosangrafted cellulose films. Based on the results, the counterplot of grafted cellulose vs shows that both variables have the same influence, where the more grafted cellulose and added to the composite, the more adsorption capacity of the chitosan-grafted cellulose/ film. The results from determining the composition, obtained the best composition, namely chitosan; grafted cellulose; each of .

Introductions

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In the fabrication of adsorbents, the applications of biopolymers for reducing water pollution were a fascinating issue [1]. According to Chen et al. (2019) and Rafiee & Rezaie Karder (2020), chitosan is a very common biopolymer used in coagulation, precipitation, flocculation, filtering, adsorption, and membrane production processes [2,3]. Chitosan is a useful biosorbent for resolving aquatic environmental contamination because of its advantages. In comparison to commercial activated carbon, chitosan has a stronger ability to bind or interact with contaminants, particularly dyes and heavy metal ions [4]. Due to its protolytic nature, chitosan still performs poorly as an adsorbent for cationic contaminants at acidic pH levels below pKa (6.2) [5]. In addition, chitosan's mechanical characteristics still need to be improved in order for it to be used more effectively.
According to Chen et al. (2019), chitosan modification increased tensile strength by [6]. Additionally, it was noted that modifying chitosan with efficiently decreased the dye concentration by after 30 hours [7]. Excellent photocatalytic abilities can be found in has been extensively developed as a photocatalyst with a large surface area and high porosity for the treatment of wastewater [8]. Through the adsorption-photodegradation process, the features of chitosan and the properties of can be combined to produce multifunctional composites with high photocatalytic capabilities and biocompatibility that exhibit good multifunctional activity [9].
To produce composites with optimum composition and to know the factors influencing of the composition, we use the Box-Behnken design method to determine the composition of the the chitosan-grafted cellulose/ composite. The software Minitab 18 was utilized to perform this composition optimization.

Materials and Methods

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Materials

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In this study, grafted cellulose (obtained from previous study [10]), chitosan (obtained from Tokyo Chemical Industry Co., Ltd., Japan, deacetylated to a level of , and molecular weight), distilled water, methylene blue, and glacial acetic acid were purchased from Merck in Germany.

Methods

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Design to formulate chitosan-grafted cellulose/TiO2 films

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Chitosan was dissolved in 100 mL of acetic acid and stirred at a speed of 250 rpm for two hours using a magnetic stirrer. Grafted cellulose was added and mixed again for two hours after the mixture was homogenous. The mixture was eventually added with distilled water-dispersed and mixed for another two hours. It was then put onto a film printer and dried for 48 hours in a oven. RSM technique along with Box-Behnken creation is used in the production of the chitosangrafted cellulose film. This design makes use of the Minitab 18 program, which has three levels: low, medium, and high. Table 1, shows the components of chitosan, grafted cellulose, and .

Table 1. Design variations in the composition of chitosan, grafted cellulose and

Run | Chitosan | Grafted cellulose | TiO₂
1 | 0.8 | 0.05 | 0.1
2 | 0.9 | 0.05 | 0.1
3 | 0.8 | 0.2 | 0.1
4 | 0.9 | 0.2 | 0.1
5 | 0.8 | 0.1 | 0.05
6 | 0.9 | 0.1 | 0.05
7 | 0.8 | 0.1 | 0.2
8 | 0.9 | 0.1 | 0.2
9 | 0.85 | 0.05 | 0.05
10 | 0.85 | 0.2 | 0.05
11 | 0.85 | 0.05 | 0.2
12 | 0.85 | 0.2 | 0.2
13 | 0.85 | 0.1 | 0.1
14 | 0.85 | 0.1 | 0.1
15 | 0.85 | 0.1 | 0.1

Table 2. Design level for preparing chitosan-grafted cellulose/ films

Factor(x) | Parameter | Level
|  | low | medium | high
x₁ | chitosan(g) | 0.8 | 0.85 | 0.9
x₂ | Grafted cellulose(g) | 0.05 | 0.1 | 0.2
x₃ | TiO₂(g) | 0.05 | 0.1 | 0.2

Determination of the optimum composition of chitosan-grafted cellulose/TiO2 film.

The original version of this paper is available on https://www.scientific.net/EC.8.65.pdf

Determination of the optimum composition of chitosan-grafted cellulose/ film was carried out using the Box-Behnken Design method from RSM. This method is carried out by combining factorial group design [11]. This design uses three factors and three levels. Adsorption capacity data from the adsorption process and percent removal from the photocatalyst process are used as responses in determining the optimum composition of chitosan-grafted cellulose/ films. To obtain this data, the following stages were carried out.
A total of 0.1 gram of chitosan-grafted cellulose film with the composition as in Table 1 which had been cut into pieces was put into 20 mL of 30 ppm methylene blue solution in a 250 mL

Erlenmeyer. Next, stir using a shaker for 60 minutes at a speed of 250 rpm . The resulting filtrate was then measured for absorbance using a UV-Vis spectrophotometer with a maximum wavelength. Next, the final concentration of the solution is calculated using a linear regression equation on the resulting calibration curve. The above procedure was repeated again with a different methylene blue removal process treatment, namely by using a UV lamp at a wavelength of 254 nm to calculate the percent removal of methylene blue.

Results and Discussions

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Optimum Composition of Chitosan-Grafted Cellulose/ TiO2 Film on MB Adsorption Capacity

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Determination of the optimum composition of chitosan-grafted cellulose/ films using the Box Behnken Design method by entering data on the adsorption capacity (Qe) of each film that has been tested for its adsorption capacity of MB. The data can be seen in the Table 3. Adsorption capacity and percent removal were obtained using equation (1) and (2).

Table 3. Data on adsorption capacity and percent removal for each composite

Run | Qe (mg/g) | %R
1 | 6.5816 | 73.42%
2 | 5.6941 | 70.90%
3 | 6.3106 | 78.76%
4 | 6.5134 | 79.14%
5 | 6.0253 | 70.78%
6 | 5.9001 | 67.95%
7 | 6.5404 | 83.28%
8 | 5.8542 | 82.71%
9 | 6.1568 | 75.04%
10 | 6.3486 | 74.78%
11 | 5.6481 | 87.32%
12 | 6.7892 | 90.65%
13 | 6.2678 | 78.47%
14 | 6.2678 | 78.17%
15 | 6.2535 | 79.73%

Where , and V correspond for the initial adsorbate concentration ( ). adsorbate concentration at t time ( ), adsorbate mass ( g ), and solution volume ( L ), respectively.

Response surface design analysis in this study used Minitab 18 software with 3 factors ( x ) in 3 levels to produce 15 runs as shown in Table 1. In this method, there are three statistical design models, namely linear, square, and 2-way interaction. The 2-way interaction and linear models have a P -Value value that is closer to zero than the square model, which is 0.021 . Slight difference with the linear model which has a P-Value of 0.024 . This value indicates that the model is significant [12].
Factors that are significant in the adsorption capacity of MB are the amount (w) of chitosan, grafted cellulose, chitosan-grafted cellulose and grafted cellulose- , each of which has a P -Value of less than 0.050 . Based on the lowest P -Value value for significant factors, grafted cellulose had a better contribution to MB adsorption capacity. Table 4 is an ANOVA analysis in this study.

Table 4. ANOVA analysis of the adsorption capacity response to MB on chitosan-grafted cellulose/ films

Source | DF | Adj SS | Adj MS | F-Value | P-Value
Model | 9 | 1.43926 | 0.159917 | 6.79 | 0.024
Linear | 3 | 0.74508 | 0.248361 | 10.54 | 0.013
Chitosan | 1 | 0.28125 | 0.281250 | 11.94 | 0.018
grafted cellulose | 1 | 0.44462 | 0.444625 | 18.88 | 0.007
TiO2 | 1 | 0.01921 | 0.019208 | 0.82 | 0.408
Square | 3 | 0.09259 | 0.030864 | 1.31 | 0.368
Chitosan*Chitosan | 1 | 0.01796 | 0.017963 | 0.76 | 0.422
grafted cellulose*grafted cellulose | 1 | 0.02498 | 0.024979 | 1.06 | 0.350
TiO2*TiO2 | 1 | 0.04488 | 0.044880 | 1.91 | 0.226
2-Way Interaction | 3 | 0.60158 | 0.200527 | 8.51 | 0.021
Chitosan*grafted cellulose | 1 | 0.29812 | 0.298116 | 12.66 | 0.016
Chitosan*TiO2 | 1 | 0.07784 | 0.077841 | 3.30 | 0.129
grafted cellulose*TiO2 | 1 | 0.22562 | 0.225625 | 9.58 | 0.027
Error | 5 | 0.11778 | 0.023556 |  |
Lack-of-Fit | 3 | 0.11770 | 0.039233 | 1005.99 | 0.001
Pure Error | 2 | 0.00008 | 0.000039 |  |
Total | 14 | 1.55704 |  |  |

Table 5. Statistical value of the adsorption capacity response to MB on chitosan-grafted cellulose films

S | R-sq | R-sq(adj) | R-sq(pred)
0.153479 | 92.44% | 78.82% | 0.00%

Based on these results, a regression of was obtained which can be seen in Table 5 . A regression value above is considered quite good. The higher the regression value, the higher the contribution and role of factor to the response . The relationship between adsorption capacity and factor ( x ) based on the coefficient value can be seen in the 3D graph in Figure 1.
The Figure 1 on the counter plot of chitosan vs grafted cellulose shows the results that the more grafted cellulose added, the better the effect on the composite adsorption capacity of MB. Chitosan, which acts as a matrix in making this composite, actually has a poor adsorption capacity for MB if the weight is too excessive. This can also be seen from the surface graph of the relationship between chitosan and grafted cellulose. Where, the darker (dense) the green color on the plot indicates the better the response. Meanwhile, the darker (deeper) the blue color on the plot indicates the smaller the influence of the variable on the response.
The Figure 1 on the counter plot of grafted cellulose vs shows that the two variables have the same influence, where the more grafted cellulose and added to the composite, the more it will influence the composite's adsorption capacity on MB. This is because these two variables have active groups which play a role in the adsorption process of cationic dyes such as MB . It can be seen in the surface graph of the relationship between grafted cellulose and that the darker green the color, the better the response produced.
The figure 1 on the counter plot of chitosan vs shows that the more added, the better the effect on the adsorption capacity of the composite on MB. However, as above, the more chitosan added, the lower the effect on the composite absorption capacity on MB.

Fig. 1. 3D graphs and surface graphs of the relationship between (a) chitosan and grafted cellulose on adsorption capacity, (b) grafted cellulose and on adsorption capacity, (c) chitosan and on adsorption capacity.

Fig. 1. 3D graphs and surface graphs of the relationship between (a) chitosan and grafted cellulose on adsorption capacity, (b) grafted cellulose and on adsorption capacity, (c) chitosan and on adsorption capacity.

The optimization results using this method provide 1 optimum composition solution for the chitosangrafted cellulose/ film based on its adsorption capacity for MB which is shown in Figure 2. Based on this result, it can be seen that the solution in the optimum composition of the composite is chitosan; grafted cellulose; of . These results can be used as a reference for preparing chitosan-grafted cellulose/ films because they have a fairly good desirability value of 0.979

Fig. 2. Solution for the optimum composition of chitosan-grafted cellulose/ film on the MB adsorption capacity of the Behnken Design Box.

Fig. 2. Solution for the optimum composition of chitosan-grafted cellulose/ film on the MB adsorption capacity of the Behnken Design Box.

Optimum composition of chitosan-grafted cellulose/film for percent removal of MB

The original version of this paper is available on https://www.scientific.net/EC.8.65.pdf

Determination of the optimum composition of chitosan-grafted cellulose/ film on the percent removal of MB was also carried out to see whether the influence of the composition was different from the influence of the composition on the adsorption capacity of the composite. Based on the results obtained, it turns out that the influence factor of the optimum composition of chitosan-grafted cellulose/ film on the percent removal of MB is different from that on the MB adsorption capacity. The percent removal value is obtained from the adsorption-photodegration process which is carried out by adding 254 nm UV light to the process. The data obtained can be seen in Table 5. The linear and square models have P -Value values that are close to zero, namely 0.002 and 0.012 . This value indicates that the model is significant [11]. Factors that are significant in the percent removal of MB are , chitosan, and grafted cellulose, each of which has a P -Value of less than 0.050. Based on the lowest P -Value value on significant factors, has a much better contribution to percent MB removal. Based on these results, a regression of was obtained which can be seen in Table 6. The relationship between percent removal and factor based on the coefficient value can be seen in the 3D graph in Figure 3.
The Figure 3 on the counterplot of chitosan vs shows that the amount of chitosan in the area of 0.82-0.88 grams has a good influence on the percent removal of MB in chitosan-grafted cellulose/ films. When the amount of chitosan added exceeds the dark green area, the effect on percent removal decreases. Meanwhile, with , the more it is added, the more the effect on percent removal increases. This is because functions as a photocatalyst in chitosan-grafted cellulose/ films, so in the photodegradation process with the help of UV light, the percentage of MB removal increases [13].
The Figure 3 on the counterplot of grafted cellulose vs shows that the two variables have the same influence, where the more grafted cellulose and added to the composite, the more it will influence the present MB removal. It can be seen in the surface graph of the relationship between grafted cellulose and that the darker green the color, the better the response produced.
The Figure 3 on the counterplot of chitosan vs grafted cellulose shows that the greater the amount of grafted cellulose added, the better the effect on the present removal of MB in the adsorptionphotodegradation process. However, as above, if the amount of chitosan added exceeds the limit in the dark green area, it will reduce the effect on the present removal.

Table 6. ANOVA analysis of the percent removal response of MB on chitosan-grafted cellulose films.

Source | DF | Adj SS | Adj MS | F-Value | P-Value
Model | 9 | 530.159 | 58.907 | 19.03 | 0.002
Linear | 3 | 422.273 | 140.758 | 45.48 | 0.000
Chitosan | 1 | 3.836 | 3.836 | 1.24 | 0.316
Grafted cellulose | 1 | 34.653 | 34.653 | 11.20 | 0.020
TiO2 | 1 | 383.784 | 383.784 | 124.00 | 0.000
Square | 3 | 101.284 | 33.761 | 10.91 | 0.012
Chitosan*Chitosan | 1 | 74.811 | 74.811 | 24.17 | 0.004
Grafted cellulose*Grafted cellulose | 1 | 5.920 | 5.920 | 1.91 | 0.225
TiO2*TiO2 | 1 | 13.207 | 13.207 | 4.27 | 0.094
2-Way Interaction | 3 | 6.601 | 2.200 | 0.71 | 0.586
Chitosan*Grafted cellulose | 1 | 2.102 | 2.102 | 0.68 | 0.447
Chitosan*TiO2 | 1 | 1.277 | 1.277 | 0.41 | 0.549
Grafted cellulose*TiO2 | 1 | 3.222 | 3.222 | 1.04 | 0.354
Error | 5 | 15.475 | 3.095 |  |
Lack-of-Fit | 3 | 14.105 | 4.702 | 6.86 | 0.130
Pure Error | 2 | 1.370 | 0.685 |  |
Total | 14 | 545.634 |  |  |

Table 7. Statistical value of the percent removal response of MB on chitosan-grafted cellulose films.

S | R-sq | R-sq(adj) | R-sq(pred)
1.75928 | 97.16% | 92.06% | 58.07%

Fig. 3. 3D graph and surface graph of the relationship between (a) chitosan and to percent removal, (b) grafted cellulose and to percent removal, (c) chitosan and grafted cellulose to percent removal.

Fig. 3. 3D graph and surface graph of the relationship between (a) chitosan and to percent removal, (b) grafted cellulose and to percent removal, (c) chitosan and grafted cellulose to percent removal.

Fig. 4. Optimum composition solution for chitosan-grafted cellulose/ film for percent MB removal from Box Behnken Design.

Fig. 4. Optimum composition solution for chitosan-grafted cellulose/ film for percent MB removal from Box Behnken Design.

Conclusions

The original version of this paper is available on https://www.scientific.net/EC.8.65.pdf

Based on the results obtained in this research, the best composite film composition design was obtained for films with Run 12, the composition of chitosan, grafted cellulose, and were 0.85 ; 0.2; 0.2 grams respectively. In determining the optimum composition for MB adsorption capacity, the most significant factor was grafted cellulose, while in determining the optimum composition for the percent removal of MB , it was . It can be concluded that determining the optimum composition design for composite films can be done using the RSM method so that the best design is produced and what factors greatly influence the response or results to be obtained. In this research, laboratory validation of the RSM results has not been carried out. These stages will be carried out in future research.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EC.8.65.pdf

The authors were grateful to Universitas Syiah Kuala for providing funding under Grant Number: 538/UN11.2.1/PT.01.03/PNBP/2023 under PRUUPD program. The authors were also grateful to those who provided suggestions when preparing this work.

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The original version of this paper is available on https://www.scientific.net/EC.8.65.pdf

H. Hassan, A. Salama, A.K. El-ziaty, and M. El-Sakhawy, "New chitosan/silica/zinc oxide nanocomposite as adsorbent for dye removal," Int. J. Biol. Macromol., vol. 131, pp. 520-526, 2019, doi: 10.1016/j.ijbiomac.2019.03.087.

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A. Gozdecka and A. E. Wiącek, "Effect of UV radiation and chitosan coating on the adsorptionphotocatalytic activity of TiO2 particles," Mater. Sci. Eng. C, vol. 93, no. July, pp. 582-594, 2018, doi: 10.1016/j.msec.2018.08.022.

H. Fathana, "Modification of Chitosan Using Glycidyl Methacrylate-grafted Cellulose (GMAgCell/Chi) for Methylene Blue Adsorption Modification of Chitosan Using Glycidyl Methacrylate-grafted Cellulose (GMAgCell/Chi) for Methylene Blue Adsorption."

W. Zhang, N. Grimi, M. Y. Jaffrin, L. Ding, B. Tang, and Z. Zhang, "Optimization of RDMUF for alfalfa wastewater treatment using RSM," Environ. Sci. Pollut. Res., vol. 25, no. 2, pp. 1439-1447, 2018, doi: 10.1007/s11356-017-0588-8.

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Engineering Chemistry, ISSN: 2813-6535, Vol. 8, pp 75-80

doi: 10.4028/p-wP8oyi

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-01-17

Revised:
2024-05-26

Accepted:
2024-05-26

Online:
2024-12-13

The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

The original version of this paper is available on https://www.scientific.net/EC.8.75.pdf

Nida Desri Effendhy , Roto Roto , Dwi Siswanta

¹Analytical Chemistry Laboratory, Chemistry Department, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia

*Corresponding author, E-mail: nida.desri.e@mail.ugm.ac.id, roto05@ugm.ac.id, dsiswanta@ugm.ac.id

Keywords: HSV, color analysis, colorimetric detection, fluoride ion, thiourea receptor

Abstract

Color analysis using colorimetric detection has shown significant advancements, although it still faces challenges when capturing color images under varying illumination conditions. This study explores the utilization of the HSV (hue, saturation, value) color model for precise color analysis, enabling the differentiation of colors based on their type, intensity, and brightness without being affected by illumination. The HSV model, measured by the Euclidean Distance ( ) value, is employed to investigate the impact of fluoride solution pH and concentration on the colorimetric detection process of fluoride ions using a thiourea receptor. The colorimetric detection of fluoride ions at low and high pH levels decreases values and notable color changes. Additionally, the HSV model elucidates the color variations induced by fluoride concentration and demonstrates a linear relationship up to 0.9809 .

Introduction

The original version of this paper is available on https://www.scientific.net/EC.8.75.pdf

Color analysis using the visual colorimetry method, which measures analyte concentration by observing the color change with the naked eye, has been developed to distinguish the color produced during detection. The development of color analysis also aims to avoid subjective factors from naked eye analysis so that insignificant color changes can be precisely distinguished [1]. Moreover, in modern society, color analysis development in colorimetric detection requires more facile, convenient, and digitalization. Hence, color analysis using digital images has been developed by employing color models such as RGB, CMYK, and HSV to represent the produced colors and to identify the colors more precisely [2].

The RGB model is the most common color model used in digital analysis, which describes the colors in a cube, with each coordinate indicating red, green, and blue colors . This model has been used mainly in the initial digital analysis of colorimetric detection because of its simplicity in describing and converting colors. However, it also means that merely based on the red, green, and blue colors will be distinguished. In addition, there is an interference in the actual condition when applying the RGB model from ambient illumination, which affects the intensity of the identified color [5].

The HSV model used in digital analysis defines color in three components: hue means color type, saturation or intensity of the color, and value means color brightness [6]. This model is also a mathematical transformation or conversion from the RGB model. Therefore, using the HSV model, color identification can be more comprehensive, not only from simple color combinations. Saturation components in the HSV model identify the color more flexibly towards the ambient illumination factor and make the color difference more accurate [5,7]. Moreover, suppose the HSV model is adapted with photoelectric colorimetry analysis like spectrophotometry. In that case, the Hue component is well-fitted to be compared with spectrophotometer wavelength because it represents the color using a single numerical value [8]. The application of the HSV model for color analysis in colorimetric detection has been reported, such as in PhCs gel sensor synthesis [9]. The sensor response to temperature and organic solvent showed linearity up to 0.95 between the Hue component and various influencing parameters, which was higher than that obtained using the RGB model.

Furthermore, high linearity was produced up to 0.993 between the Hue component and spectrophotometry usage for color analysis of pH detection by an inverse opal photogenic gel (IOPG) sensor [8].

The colorimetric detection of fluoride ions with the thiourea receptor produces a specific color from the detection process, which is also influenced by the pH and concentration of the fluoride ion solution. The color resulting from the colorimetric detection of fluoride ions can be analyzed digitally to identify color changes more efficiently and accurately [10]. Therefore, this study employed the HSV color model for digital analysis of the color produced by fluoride ion colorimetric detection using a thiourea receptor.

Experimental

The original version of this paper is available on https://www.scientific.net/EC.8.75.pdf

Materials used in this research were acetonitrile, 1,1'-(1,4-phenylene)bis(3-(4nitrophenyl)thiourea), tetra-n-butylammonium fluoride, NaOH crystals, solution. This research was conducted by adding specific amounts of tetra-n-butylammonium fluoride into a thiourea solution in acetonitrile. Afterward, the color that had been produced from the fluoride detection was performed digitally by placing the test solution in a cuvette on a lightbox illuminated with a fixed LED lamp on the top side of the box. The OPPO Reno smartphone was placed in a holder at a fixed distance from the colorimetric solution. Photographs of the solution cuvette were captured using the smartphone's built-in camera with 48 Megapixel resolution. The ColorGrab application was used to convert the HSV value from the image. The Euclidean Distance ( ) value represents the distance between the hue, saturation, and value component, which is proportional to the fluoride concentration used for quantitative analysis. The was determined using the following equation:

Results and Discussion

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The colorimetric detection of fluoride ion with thiourea receptor resulted in a color change from colorless to orange. The color change occurred because of the interaction between the NH active site on the thiourea receptor and fluoride ion, as shown in Figure 1. The positive partial of hydrogen in the NH group enabled the formation of a bond with fluoride ion, which has high electronegativity. In addition, the acidity of the thiourea receptor was strengthened with the electron-withdrawing group, , on its structure so that the NH group could donate hydrogen more efficiently [11]. A significant color change from fluoride ion detection may also be caused by thiourea receptor usage with more than two chromophore groups on its structure, which makes the surrounding electrons more delocalized.

Fig. 1. Interaction between fluoride ion with thiourea receptor resulted in color change

Color analysis was performed using the HSV model to observe the influence of pH and concentration on the fluoride ion detection process. All the components used in the HSV model were combined to Euclidean Distance ( ) value, which represents the distance between components and is proportional to the fluoride concentration for quantitative analysis [12]. The Euclidean distance value shows a significant reduction in the influence of acidic pH on the fluoride ion detection process with thiourea, as shown in Figure 2.

Fig. 2. Color changes and value of different fluoride solution pH condition

Fig. 2. Color changes and value of different fluoride solution pH condition

The reduction of the Euclidean distance value is caused by the formation of fluoride acid (HF), which is dominantly formed with excess hydrogen ions in solution [13]. The effect of acidic pH on the detection process does not produce a color change, so comparing HSV values at pH 5 (control) with acidic pH can be easily distinguished. The Euclidean distance value between the control and acidic and 3 experienced a significant difference because all three components of the HSV model from both pH ranges result in different values that are characteristic for each produced color.

On the other hand, under basic conditions, the fluoride ion detection process with thiourea still produces color even though the Euclidean distance value decreases. The different Euclidean distance values can be dominated by Hue or color type values, which differ from orange (control pH ) to yellow (alkaline pH ). The decrease in the value of at alkaline pH , which is not as large as that at acidic pH , is probably due to the presence of ions that bind to thiourea and reduce the acidity of its structure, thereby reducing the thiourea response to fluoride ions.

Fig. 3. Color changes and values of different fluoride solution concentrations

Fig. 3. Color changes and values of different fluoride solution concentrations

Regarding the effect of fluoride ion concentration on the detection process presented in Figure 3, the Euclidean distance value of the HSV model increased as the concentration of fluoride ions increased. These increases could be caused by the greater the concentration of fluoride ions added and the more fluoride ions interact with thiourea. At the beginning of fluoride ion addition at a small concentration, the presence of fluoride ion induces an interaction between the NH groups on thiourea and the ion. The addition of fluoride ions at higher concentrations causes deprotonation of the thiourea structure, as shown in Figure 4, resulting in a sharper and more saturated color change [11].

Fig. 4. Protonation and deprotonation structure of thiourea receptor toward fluoride addition

Fig. 4. Protonation and deprotonation structure of thiourea receptor toward fluoride addition

Based on the Euclidean distance value and the observed color shifts, the most influential component of the HSV model appears to be the Hue component. This is evident in the substantial color variations ranging from colorlessness to shades of green, yellow, and orange at various concentration points. A notable shift in color occurs between concentrations of 3.75 and 4.5, transitioning from yellow to orange. This transition is marked by a significant increase in the Euclidean distance value at both concentrations.

In contrast, at higher concentrations exceeding 7.5 mol , it is conceivable that the NH group in thiourea attains full occupancy, rendering it unresponsive to or unable to interact with fluoride ions. Consequently, the orange color observed loses its vibrancy to the naked eye. Nonetheless, the color shift remains discernible utilizing HSV analysis through the Euclidean distance.

The analysis of the HSV model indicates strong compatibility with different fluoride ion concentrations, showing a linearity of up to 0.9809 . This compatibility suggests that the HSV model is highly effective in elucidating and distinguishing the color changes that occur during the fluoride ion detection process with thiourea.

Conclusion

The original version of this paper is available on https://www.scientific.net/EC.8.75.pdf

The HSV color model has been used to analyze the color resulting from fluoride ion detection using the thiourea receptor, which produces a color change from colorless to orange. On the effect of pH in the detection process, the analysis was carried out using the HSV model, represented by the Euclidean distance ( ) value, and shows a decrease in both acidic and basic pH levels, corresponding to the resulting color change. Using the HSV model, the effect of fluoride ion concentration is also explained by increases in the value as the fluoride ion concentration increases with a linearity of 0.9809. Therefore, it can be concluded that the HSV color model can accurately describe the color change under the influence of pH and concentration. It shows the convenience in the colorimetric analysis of fluoride ion detection with the thiourea receptor.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EC.8.75.pdf

The authors would like to express their sincere gratitude for the financial support provided for this research. The study received funding under the Penelitian Dasar Unggulan Perguruan Tinggi grant with the reference number 1682/UN1/DITLIT/DIT-LIT/PT/2021, which was awarded by the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia. This support was instrumental in facilitating the successful execution of the research project.

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Keywords Index

A

Activated Thermal Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

Adsorption Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes; Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

Adsorption Capacity Recyclability and Carbonization Regeneration of Oil Adsorbing Materials Based on Commercial Expandable Graphite

Adsorption Isotherm Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

Adsorption Kinetics Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

Adsorption Kinetics; Pseudo-first-order; Pseudo-second-order; Competition Parameter; Multicomponent System Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

C

Carbonization Regeneration Recyclability and Carbonization Regeneration of Oil Adsorbing Materials Based on Commercial Expandable Graphite

Characteristics Serpentine Aceh Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

Chloric Acid Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

Clay; Bentonite; Zeolite; Monolith; Adsorption Advancing Environmental Protection: Monolithic Bentonite and ZeoliteClay Adsorbents for Ion Removal

Color Analysis The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

Colorimetric Detection The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

Copper Ions Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

E

Expanded Graphite Recyclability and Carbonization Regeneration of Oil Adsorbing Materials Based on Commercial Expandable Graphite

F

Fluoride Ion The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

G

Graphene Oxide Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

H

Hsv The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

M

Methylene Blue Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

O

Oil Adsorbing Material Recyclability and Carbonization Regeneration of Oil Adsorbing Materials Based on Commercial Expandable Graphite

P

Patchouli Dregs Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

Pyrolysis Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

R

Recyclability Recyclability and Carbonization Regeneration of Oil Adsorbing Materials Based on Commercial Expandable Graphite

S

Sem-eds Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

T

Thiourea Receptor The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

X

Xrf Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

Authors Index

A

Adisalamun, A. Advancing Environmental Protection: Monolithic Bentonite and ZeoliteClay Adsorbents for Ion Removal

Adlim, M. Determination of the Optimum Composition of Chitosan-Grafted Cellulose/TiO2 Films Using the Box Behnken Design Method

C

Chairunnisak, A. Advancing Environmental Protection: Monolithic Bentonite and ZeoliteClay Adsorbents for Ion Removal

D

Darmadi, D. Advancing Environmental Protection: Monolithic Bentonite and ZeoliteClay Adsorbents for Ion Removal

Denesa, E. Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

E

Effendhy, N. The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

F

Fathana, H. Determination of the Optimum Composition of Chitosan-Grafted Cellulose/TiO2 Films Using the Box Behnken Design Method

Fazira, Z. Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

G

Gani, A. Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes; Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

H

Hasyim, M. Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

I

Irawaty, W. Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

Ismadji, S. Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

L

Laysandra, T. Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

Li, C. Recyclability and Carbonization Regeneration of Oil Adsorbing Materials Based on Commercial Expandable Graphite

Lubis, S. Determination of the Optimum Composition of Chitosan-Grafted Cellulose/TiO2 Films Using the Box Behnken Design Method

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Magallanes, P. Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

Mahidin, M. Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

Muchtar, S. Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

Mulana, F. Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

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Ongayo, G. Efficient Removal of Aqueous Copper (II) lons Using EDTA-Modified Graphene Oxide: An Adsorption Study

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Pratama, W. Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

Putra, E. Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

Putro, J. Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

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Rahmi, R. Determination of the Optimum Composition of Chitosan-Grafted Cellulose/TiO2 Films Using the Box Behnken Design Method

Reza, A. Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

Roto, R. The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

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Santoso, S. Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

Siswanta, D. The Application of the HSV Color Model for Accurate Digital Colorimetric Analysis of Fluoride Detection Using a Thiourea Receptor

Soetaredjo, F. Binary Adsorption Kinetics of Tetracycline and Doxycycline on HKUST-1: Modification Pseudo First and Second Order Models

Suraiya, S. Characterization of Bio-Sorbent from the Pyrolysis of Patchouli Dregs to Remove Methylene Blue Dyes

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Wardani, S. Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

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Yunardi, Y. Characteristics Serpentine Aceh; Morphology and Chemical Compositions Studied by SEM-EDS and XRF

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Zaki, M. Advancing Environmental Protection: Monolithic Bentonite and ZeoliteClay Adsorbents for Ion Removal